**Learning Performance and Satisfaction on Working Education**

Chun-Ling Ho and Tsung-Han Chang *Kao Yuan University Taiwan* 

#### **1. Introduction**

46 E-Learning – Engineering, On-Job Training and Interactive Teaching

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The emerging Internet and World Wide Web (WWW) brought about fast variations in the development of the learning process in the past ten years. There are various forms for learning, such as Computer-Aided Instructions (CAI), Intelligent Tutoring System (ITS), to Web-Based Learning (WBL), and e-learning systems. In present fast changing electronic world (e-world) knowledge is the key to maintaining the appropriate impetus and momentum in organizational and academic environments. In this situation continuous, convenient and economical access to training and qualifications assumes the highest priority for the ambitious individual or organization. With the booming development of Internet and information technology, the Internet has broken the limitation of time and space. Information and Communication Technology (ICT) have recently affected strongly on every field in the society; especially in recent years, e-learning is being applied widely in the areas of training around the world.

Generally speaking, e-learning is a mode of education that builds on a network technologybased and also uses a mix of computer and other ICTs, across time and space restricted to deliver instruction and provide access to information resources. It can be included delivery systems such as videotape, interactive audio-video, CD-ROMs, DVDs, video-conferencing, VOD, e-mail, live chat, use of the Web, television, satellite broadcasts and so on. It also includes the delivery of contents through Internet, intranet/extranet, audio and videotape, satellite broadcast, interactive TV, and CD-ROM". Access to these resources means that students can do homework at a time they feel free and convenience, therefore learning may conduct synchronously or asynchronously.

E-learning can provide content and knowledge as valuable as like traditional training environment. Conventional learning often requires learners to travel to different locations and gather in various classrooms at specific time, but e-learning has no such restrictions. Meeting face-to-face is no longer necessary and learners just need to meet with each other via electronic modes of delivery (e.g., chat rooms, discussion boards, instant messaging). The benefits of e-learning are not only to learners but also the organizations when they learned form this education training mode.

Taiwan in 1999 had the "Fundamental Science and Technology" concept to establish fundamental principles and directions for the development of science and technology. The

Learning Performance and Satisfaction on Working Education 49

In the face of no decrease occupational injuries, attaining a high and consistent level of safety-health management system is becoming an important issue for manufacturers and industry. According to the statistics, the major occupation accident rate of construction has been the highest among all industries. In Taiwan, the percentage of deaths from the construction professional accidents between 2001 and 2010 is around 0.031%, much higher than that during the same period in advanced countries in Europe, America and the UK, indicating that the situation of construction professional accidents in construction has become a very serious problem. Thus it's imperious to improve the construction worker

On the other hand, the working injury statistics revealed by Council of Labor Affairs in Taiwan, 75% of the casualties were caused by worker's unsafe behavior, and 75% of the unsafe behavior related injured workers were not properly trained by the employers on safety. It means working safety training will lower injuries and plays most significant role in reducing unsafe behaviors. Therefore, how to improve the effect of safety training is one of

The general safety training courses in most companies still follow traditional oral teaching method and lack of discussion, practice, and simulation drill. And in order to reduce training cost, most of instructors are in-house employees that provide the boring courses. It will decrease the effect of training. This chapter derives the training and e-learning for labor safety and by analyzing the survey and setting up the e-learning procedures, it also establishes a good labor safety training planning and implementation mechanism which can

This chapter obtained the valid samples from A construction and the purpose is to explore the relationship among education training and the relationship after training by elearning. There were totally 185 questionnaires issued in this chapter, and 178 questionnaires are valid (effective sample rate is 96.2%). First, responses from the questionnaires were gathered and entered into SPSS 16. The significance level chosen for this study was .05. Descriptive statistics on all the data provided frequencies, percentages,

The characteristics of this sample were calculated including age and gender of using e-Learning for labor working safety training by SPSS that also conduct Descriptive statistical analysis, Factor analysis, Reliability analysis, Analysis of variance, T-test, Duncan multiple T-comparison, and Regression analysis to probe this study. The reliability of data also

The sample consisted of 178 workers with 43.6% female and 54.4% male. For age, 12.3% of total respondents were over 50 years old, with about 28.6% in the age of 50-40 years old,

about 38.9% in the age of 40-30 years old and about 20.2% in 30-20 years old.

safety management, in order to decrease the construction accident occurrence.

be conformed the requirements of laws and regulation in construction.

**2. Working safety training** 

the key factors to reduce working injuries.

**3. Proposed model and hypotheses** 

means, and standard deviations.

assessed by computing Cronbach's alpha.

**3.1 Sample** 

same year the government approved NTD 40 million within a 5-year period for the "National Science and Technology Program for e-Learning". Firms are aware of the need to become learning organizations and increase workers' skills in order to accommodate new technologies. The e-Learning offers not only the way for anytime, anywhere, flexible learning online, but is also a cost effective and flexible method, and one the public and private sectors have taken as a useful tool to train and educate the workforce.

For enterprises, e-Learning could be savings, increasing worker productivity, driving operational efficiencies, and streamlining corporate training. e-Learning initiative Basic Blue program in IBM, it would save \$16 million in 2000. And after cooperating with their e-Learning management system-Platue Systems, the American Red Cross saved more than \$10 million in seven-years. About Toyota Motor Sales USA in 2002 declared that the use of the Learning System to strengthen training, it would save more than \$11.9 million in five years. Worldwide revenues in the e-Learning market will reach US\$ 500 billion by 2010, and the growth of e-Learning market is expected to multiply by 6.

Learning Environment online is one of key factors that increase the learning satisfaction. e-Learning platform should include Content Management System (CMS) and Learning Management Systems (LMS). Modular Object-Oriented Dynamic Learning Environment (MOODLE) is a free and open source e-learning software platform and is designed to help educators create online courses with opportunities for rich interaction. It opens source license and modular design that people can develop additional functionality. Basing on MOODLE, e-learning system provides help and supports to learners through diverse technologies including real-time chat, messages boards, email, lecture material files, and so on. According to Clark and Mayer (2003), e-learning was defined as instruction delivered on a computer by way of CD-ROM, Internet, or Intranet with the following features:


This chapter proposes a theoretical model based on working safety training in construction and integrates the adoption and satisfaction of e-learning by labors. The objective are threefold:


This chapter attempts to construct a conceptual model and then integrate the some external factors into the proposed model. The objectives/questions include the following:

Question 1: What are the factors that significantly influence labors using the technology in elearning environments?

Question 2: How does the proposed model explain the variances of satisfaction?

#### **2. Working safety training**

48 E-Learning – Engineering, On-Job Training and Interactive Teaching

same year the government approved NTD 40 million within a 5-year period for the "National Science and Technology Program for e-Learning". Firms are aware of the need to become learning organizations and increase workers' skills in order to accommodate new technologies. The e-Learning offers not only the way for anytime, anywhere, flexible learning online, but is also a cost effective and flexible method, and one the public and

For enterprises, e-Learning could be savings, increasing worker productivity, driving operational efficiencies, and streamlining corporate training. e-Learning initiative Basic Blue program in IBM, it would save \$16 million in 2000. And after cooperating with their e-Learning management system-Platue Systems, the American Red Cross saved more than \$10 million in seven-years. About Toyota Motor Sales USA in 2002 declared that the use of the Learning System to strengthen training, it would save more than \$11.9 million in five years. Worldwide revenues in the e-Learning market will reach US\$ 500 billion by 2010, and

Learning Environment online is one of key factors that increase the learning satisfaction. e-Learning platform should include Content Management System (CMS) and Learning Management Systems (LMS). Modular Object-Oriented Dynamic Learning Environment (MOODLE) is a free and open source e-learning software platform and is designed to help educators create online courses with opportunities for rich interaction. It opens source license and modular design that people can develop additional functionality. Basing on MOODLE, e-learning system provides help and supports to learners through diverse technologies including real-time chat, messages boards, email, lecture material files, and so on. According to Clark and Mayer (2003), e-learning was defined as instruction delivered on a computer by way of CD-ROM, Internet, or Intranet with the following


This chapter proposes a theoretical model based on working safety training in construction and integrates the adoption and satisfaction of e-learning by labors. The objective are

3. To make sure how factors in the proposed model influence labors' learning in working

This chapter attempts to construct a conceptual model and then integrate the some external

Question 1: What are the factors that significantly influence labors using the technology in e-

factors into the proposed model. The objectives/questions include the following:

Question 2: How does the proposed model explain the variances of satisfaction?

private sectors have taken as a useful tool to train and educate the workforce.

the growth of e-Learning market is expected to multiply by 6.



2. To identify the factors affecting using e-learning system.

1. To conceptualize a theoretical structural model based on e-learning.

features:

threefold:

performance.

learning environments?

safety training of e-learning.

In the face of no decrease occupational injuries, attaining a high and consistent level of safety-health management system is becoming an important issue for manufacturers and industry. According to the statistics, the major occupation accident rate of construction has been the highest among all industries. In Taiwan, the percentage of deaths from the construction professional accidents between 2001 and 2010 is around 0.031%, much higher than that during the same period in advanced countries in Europe, America and the UK, indicating that the situation of construction professional accidents in construction has become a very serious problem. Thus it's imperious to improve the construction worker safety management, in order to decrease the construction accident occurrence.

On the other hand, the working injury statistics revealed by Council of Labor Affairs in Taiwan, 75% of the casualties were caused by worker's unsafe behavior, and 75% of the unsafe behavior related injured workers were not properly trained by the employers on safety. It means working safety training will lower injuries and plays most significant role in reducing unsafe behaviors. Therefore, how to improve the effect of safety training is one of the key factors to reduce working injuries.

The general safety training courses in most companies still follow traditional oral teaching method and lack of discussion, practice, and simulation drill. And in order to reduce training cost, most of instructors are in-house employees that provide the boring courses. It will decrease the effect of training. This chapter derives the training and e-learning for labor safety and by analyzing the survey and setting up the e-learning procedures, it also establishes a good labor safety training planning and implementation mechanism which can be conformed the requirements of laws and regulation in construction.

#### **3. Proposed model and hypotheses**

#### **3.1 Sample**

This chapter obtained the valid samples from A construction and the purpose is to explore the relationship among education training and the relationship after training by elearning. There were totally 185 questionnaires issued in this chapter, and 178 questionnaires are valid (effective sample rate is 96.2%). First, responses from the questionnaires were gathered and entered into SPSS 16. The significance level chosen for this study was .05. Descriptive statistics on all the data provided frequencies, percentages, means, and standard deviations.

The characteristics of this sample were calculated including age and gender of using e-Learning for labor working safety training by SPSS that also conduct Descriptive statistical analysis, Factor analysis, Reliability analysis, Analysis of variance, T-test, Duncan multiple T-comparison, and Regression analysis to probe this study. The reliability of data also assessed by computing Cronbach's alpha.

The sample consisted of 178 workers with 43.6% female and 54.4% male. For age, 12.3% of total respondents were over 50 years old, with about 28.6% in the age of 50-40 years old, about 38.9% in the age of 40-30 years old and about 20.2% in 30-20 years old.

Learning Performance and Satisfaction on Working Education 51

A survey questionnaire was developed to measure the relevant constructs. Table 1 summarizes the operational definition as well as the references for each construct. A fivepoint Likert-type scale asked the subjects to rate the degree to which they agreed with the statements on a 1 to 5 scale-1 represented "strongly disagree" and 5 "strongly agree". And

In verifying the scale for measuring these constructs, Cronbach's alpha was used to assess the reliability. The coefficient alpha values for user interface, rich content, platform function, learning support were 0.92, 0.81, 0.90, 0.93 and 0.87. Because the Cronbach's alpha values were above the conventional level of 0.7 (Nunnally, 1978), the scales for these constructs

It conducted a confirmatory factor analysis (CFA) to test the convergent validity of each construct. The loadings of items against the construct being measured were tested against the value 0.7 on the construct being measured and table 2 showed the results obtained for the loadings in relation to the latent variables. The factors structure in factor analysis went

> Component 1 2 3 4 5

Table 2 presents the items and the respective loadings of the instrument.

**4. Data analysis** 

**4.1 Reliability and factor analysis** 

were deemed to exhibit adequate reliability.

well with the structure of the questionnaire.

U1 **0.8912**  U2 **0.8673**  U3 0.3767

Table 2. Initial values of loadings

R4 **0.9022**  R5 **0.7891**  R6 0.5236

P7 **0.7816**  P8 **0.8603**  P9 **0.7928** 

S13 **0.7837**  S14 **0.8619** 

L10 0.6156 L11 **0.8993**  L12 **0.8762** 

Item

#### **3.2 Instrument development**

On the questionnaire, four predictors-user interface, rich content, platform function, learning support to satisfaction on e-learning training, and two labor variables-age and gender were selected for further investigation. Figure 1 depicts the hypotheses of three groups. Every group shows the hypotheses to examine the effect of perceptions.

H1: System design (user interface, rich content, platform function, learning support) are positively related to satisfaction on e-learning training.

H2: System design (user interface, rich content, platform function, learning support) are correlated with labor variables of age and gender.

H3: Satisfaction on e-learning training is correlated with labor variables of age and gender.

Fig. 1. The framework of Satisfaction on e-learning training


Table 1. Variable Definition

A survey questionnaire was developed to measure the relevant constructs. Table 1 summarizes the operational definition as well as the references for each construct. A fivepoint Likert-type scale asked the subjects to rate the degree to which they agreed with the statements on a 1 to 5 scale-1 represented "strongly disagree" and 5 "strongly agree". And Table 2 presents the items and the respective loadings of the instrument.

#### **4. Data analysis**

50 E-Learning – Engineering, On-Job Training and Interactive Teaching

On the questionnaire, four predictors-user interface, rich content, platform function, learning support to satisfaction on e-learning training, and two labor variables-age and gender were selected for further investigation. Figure 1 depicts the hypotheses of three

H1: System design (user interface, rich content, platform function, learning support) are

H2: System design (user interface, rich content, platform function, learning support) are

H3: Satisfaction on e-learning training is correlated with labor variables of age and gender.

U2 Friendly interface U3 Colorful elements

P8 Playing easily

R5 Suitable training content R6 Tools for fast learning

P9 Good network connection

S13 Increase the learning times by myself

L11 Help function design L12 Supporting online tutor

S14 Intensive training effect

groups. Every group shows the hypotheses to examine the effect of perceptions.

positively related to satisfaction on e-learning training.

Fig. 1. The framework of Satisfaction on e-learning training

Rich content R4 Multi-media for design

Platform function P7 Function with well done

Learning support L10 Given learning direction

Team Item Measure User interface U1 Easy to use

Satisfaction on e-learning training

Table 1. Variable Definition

correlated with labor variables of age and gender.

**3.2 Instrument development** 

#### **4.1 Reliability and factor analysis**

In verifying the scale for measuring these constructs, Cronbach's alpha was used to assess the reliability. The coefficient alpha values for user interface, rich content, platform function, learning support were 0.92, 0.81, 0.90, 0.93 and 0.87. Because the Cronbach's alpha values were above the conventional level of 0.7 (Nunnally, 1978), the scales for these constructs were deemed to exhibit adequate reliability.

It conducted a confirmatory factor analysis (CFA) to test the convergent validity of each construct. The loadings of items against the construct being measured were tested against the value 0.7 on the construct being measured and table 2 showed the results obtained for the loadings in relation to the latent variables. The factors structure in factor analysis went well with the structure of the questionnaire.


Table 2. Initial values of loadings

Learning Performance and Satisfaction on Working Education 53

As illustrates in Table 5, age level had many significant influence on the user interface, rich content and platform function (p-value = 0.00), and 20-50 years old generally make more

Over 50 years old 1.13 4.89 5.26 0.57 50-40 years old 4.36 5.02 4.63 4.17 40-30 years old 5.39 5.36 5.14 4.06 30-20 years old 5.17 5.89 4.37 4.82 t-statistic 4.69 4.12 4.33 -0.86 df 57.63 57.09 57.82 51.87 p-value 0.00 0.00 0.00 0.68

The analyses of data include descriptive statistics and Structural Equation Modeling (SEM). SEM with LISREL 8.5 will be used to analyze the data from the respondents. It has a number of advantages over multiple regressions which is commonly used to validate aspects of the theory. The SEM consists of two parts, and they are the structural model and the measurement model. The structural model shows potential causal dependencies between endogenous and exogenous variables, and the measurement model shows the relations between the latent variables and their indicators. The structural model was evaluated using

content

User interface Rich

Table 5. The relationship between system design and age

**4.3 Structural model evaluation** 

the following criteria:

a. Ability to explain variance b. Significance of path coefficients

Fig. 2. Model with T-value path coefficients

Mean

Platform function

Learning support

interesting of all means.

Based on the criteria that item loadings greater than 0.70, we analysis of the cognitive absorption construct shows that all items, and then U3, R6 and L10 are much lower than acceptable. These two items were dropped from the final model.

Once all the items that did not load satisfactorily had been removed, the model was rerun. Table 3 shows the results of testing the measurement model in the final run. The t-values for model loadings show that model loadings are all above 1.96 and significant.


Table 3. Final values of loadings

#### **4.2 The relationship between System design and labor variables**

For understanding the relationship, in case between gender and user interface, rich content, platform function, learning support by using T-test. The result showed both gender had significant to perceived each mean (p-value), and the result revealed that male (mean=5.03) and female (mean=4.34). It is indicates in Table 4.


Table 4. The relationship between system design and gender

Based on the criteria that item loadings greater than 0.70, we analysis of the cognitive absorption construct shows that all items, and then U3, R6 and L10 are much lower than

Once all the items that did not load satisfactorily had been removed, the model was rerun. Table 3 shows the results of testing the measurement model in the final run. The t-values for

> Component 1 2 3 4 5

acceptable. These two items were dropped from the final model.

T Statistics SD Item

Table 3. Final values of loadings

8.9035 0.0357 U1 **0.8912** 7.0981 0.0218 U2 **0.8673**

12.4582 0.0563 R4 **0.8972** 7.0972 0.0371 R5 **0.7633**

model loadings show that model loadings are all above 1.96 and significant.

14.3156 0.0936 P7 **0.7655**  22.9064 0.0367 P8 **0.8413**  25.0673 0.0655 P9 **0.7836** 

14.3196 0.0348 S13 **0.7837**  9.3681 0.0762 S14 **0.8619** 

For understanding the relationship, in case between gender and user interface, rich content, platform function, learning support by using T-test. The result showed both gender had significant to perceived each mean (p-value), and the result revealed that male (mean=5.03)

> Rich content

Male 4.09 6.12 5.73 4.36 Female 4.35 5.02 4.63 4.17 t-statistic 3.26 4.15 3.87 4.56 df 212.95 214.33 214.76 214.28 p-value 0.00 0.00 0.00 0.00

Mean

Platform function

Learning support

10.3528 0.0887 L11 **0.8763** 13.7835 0.0523 L12 **0.8359**

**4.2 The relationship between System design and labor variables** 

and female (mean=4.34). It is indicates in Table 4.

User interface

Table 4. The relationship between system design and gender

As illustrates in Table 5, age level had many significant influence on the user interface, rich content and platform function (p-value = 0.00), and 20-50 years old generally make more interesting of all means.


Table 5. The relationship between system design and age

#### **4.3 Structural model evaluation**

The analyses of data include descriptive statistics and Structural Equation Modeling (SEM). SEM with LISREL 8.5 will be used to analyze the data from the respondents. It has a number of advantages over multiple regressions which is commonly used to validate aspects of the theory. The SEM consists of two parts, and they are the structural model and the measurement model. The structural model shows potential causal dependencies between endogenous and exogenous variables, and the measurement model shows the relations between the latent variables and their indicators. The structural model was evaluated using the following criteria:


Fig. 2. Model with T-value path coefficients

Learning Performance and Satisfaction on Working Education 55

It can be seen in the above figure, the T-value for eight factors are more than 1.96 suggested by Byrne (2001). H1a, H1b, H1c, H1d, H2a, H2b, H2f, H3a are supported by the data and the other hypotheses (H2c, H2d, H2e and H3b) are rejected. Significant differences were found in two groups of H1 and H2. Significant differences appeared in the path coefficients of all, they are system design and satisfaction on e-learning training, user interface and age, rich

The results found in fact, for satisfaction on e-learning training, it has a substantially greater effect on system design. Both of their coefficients are significant, validating H1. H3 assumes the perceptions of labor variable are positively related to satisfaction on e-learning training. In this case illustrate partly influence between them, thus the results do not provide support for H3. H2 posit that system design attitude would have an affect on labor variable. However, findings illustrate only perceived "user interface and rich content" shows

content and age, rich content and gender, age and satisfaction on e-learning training.

**5. Factors influence labors' satisfaction of e-learning in working safety** 

This section summarized some major findings which were discussed with the aims of the

Majority of respondents on age of labor variable, it were between 30-50 years old with

 This case study found no direct effect on gender satisfaction and system design, but only a significant effect on rich content of system design. The non-significance of the

 System design demonstrated much significant influence with satisfaction on e-learning training, such as user interface, rich content, platform function, learning support. These

 Based on e-learning satisfaction, it identified strongly four significant predictors of system design in the structural model of the SEM test: user interface ( T-value=0.35\*\*\*), rich content ( T-value=4.012\*\*\* ), platform function( T-value=3.763\*\*\* ), and learning

H1 posit that satisfaction and system design would have a positive relation to e-

H2 posit that system design and labor variable would have a positive relation to age of

H3 posit that labor variable and satisfaction would have no relation to e-Learning

Recently, e-Learning has become an important teaching method, and to implement an elearning system can make the whole training process to go through it smoothly without any limitation of time and space. Therefore, e-Learning has become the revolution in the 21st century, and it is not only the learning tendency in the future, but also the important part to

significant coefficient. Therefore, the findings do not totally support H2.

direct effect is consistent with other recent researches.

finding are also consistent with the prior studies.

**6. Discussion on e-Learning and working training** 

**training** 

chapter:

positively related to satisfaction.

support ( T-value=3.002\*\*\* ).

Learning systems.

labors.

systems.

The structural model was tested with the data from the entire data sample and each of labor variables respectively. The comparison of path coefficients are between system design and satisfaction on e-learning training, labor variables and satisfaction on e-learning training, system design and labor variables (shown as Fig.2). The significance of difference was calculated in Table 6 and Table 7. Following the figure show the results of SEM, include standardized, estimated and T-value model, as well.


\*p <0.05 (one-tailed test)

\*\*p <0.01 (one-tailed test)

\*\*\*p <0.001 (one-tailed test)

Table 6. Hypotheses test


\*p <0.05 (one-tailed test)

\*\*p <0.01 (one-tailed test)

\*\*\*p <0.001 (one-tailed test)

Table 7. H1 to H3 hypotheses test results

The structural model was tested with the data from the entire data sample and each of labor variables respectively. The comparison of path coefficients are between system design and satisfaction on e-learning training, labor variables and satisfaction on e-learning training, system design and labor variables (shown as Fig.2). The significance of difference was calculated in Table 6 and Table 7. Following the figure show the results of SEM, include

Hypotheses Effects T-values Result

training 3.256\*\*\* Supported

training 4.012\*\*\* Supported

training 3.763\*\*\* Supported

training 3.002\*\*\* Supported

H2a user interface <-> age 4.786\*\*\* Supported H2b rich content <-> age 3.267\*\*\* Supported H2c platform function <-> age -0.162 Not supported H2d learning support <-> age 0.268 Not supported H2e user interface <-> gender 0.157 Not supported H2f rich content <-> gender 2.767\*\*\* Supported H2g platform function <-> gender -2.031 Not supported H2h learning support <-> gender 0.365 Not supported H3a age <-> satisfaction on e-learning training 4.629\*\*\* Supported H3b gender <-> satisfaction on e-learning training 1.903 Not supported

> Standardized Path Coefficients

H1 0.721\*\* Supported H2 0.893\*\* Supported H3 0.126 Not supported

Hypotheses Supported

standardized, estimated and T-value model, as well.

\*p <0.05 (one-tailed test) \*\*p <0.01 (one-tailed test) \*\*\*p <0.001 (one-tailed test)

Table 6. Hypotheses test

Model hypotheses

\*p <0.05 (one-tailed test) \*\*p <0.01 (one-tailed test) \*\*\*p <0.001 (one-tailed test)

Table 7. H1 to H3 hypotheses test results

H1a user interface<-> satisfaction on e-learning

H1b rich content <-> satisfaction on e-learning

H1c platform function <-> satisfaction on e-learning

H1d learning support <-> satisfaction on e-learning

It can be seen in the above figure, the T-value for eight factors are more than 1.96 suggested by Byrne (2001). H1a, H1b, H1c, H1d, H2a, H2b, H2f, H3a are supported by the data and the other hypotheses (H2c, H2d, H2e and H3b) are rejected. Significant differences were found in two groups of H1 and H2. Significant differences appeared in the path coefficients of all, they are system design and satisfaction on e-learning training, user interface and age, rich content and age, rich content and gender, age and satisfaction on e-learning training.

The results found in fact, for satisfaction on e-learning training, it has a substantially greater effect on system design. Both of their coefficients are significant, validating H1. H3 assumes the perceptions of labor variable are positively related to satisfaction on e-learning training. In this case illustrate partly influence between them, thus the results do not provide support for H3. H2 posit that system design attitude would have an affect on labor variable. However, findings illustrate only perceived "user interface and rich content" shows significant coefficient. Therefore, the findings do not totally support H2.

#### **5. Factors influence labors' satisfaction of e-learning in working safety training**

This section summarized some major findings which were discussed with the aims of the chapter:


#### **6. Discussion on e-Learning and working training**

Recently, e-Learning has become an important teaching method, and to implement an elearning system can make the whole training process to go through it smoothly without any limitation of time and space. Therefore, e-Learning has become the revolution in the 21st century, and it is not only the learning tendency in the future, but also the important part to

Learning Performance and Satisfaction on Working Education 57

e-Learning also brings advantages of flexibility and low cost for working education so that the strong strength can not be ignored. Nevertheless there are no the theoretical essentials to how to create the learning management system according to the characteristic of the enterprises. The chapter applied SEM to explore the relation among satisfaction, labor variables and system design by analyzing factors in the e-learning environment. First of all, it will analysis the system style, such as user interface, rich content, platform function, learning support. Then we had conducted an experiment to compare the learning

To promote the training satisfaction and performance, this chapter highly suggests that the construction should understand learning characteristics and learning behavior from workers with safety classes to fulfill the needs from learner. Therefore, the three basic principles in conducting evaluation are based on system design (user interface, rich content, platform function, learning support), labor variables (age and gender) and satisfaction on e-learning

4. The key factors for positively-correlated effect are between satisfaction and system

5. For good training program, to make the e-learning cost-efficient and to integrate

According to the chapter, a successful e-learning system is integrated with the application of technology and the design of system. Therefore, this chapter will provide valuable reference

Chin, W. W., and Todd, P., 1995, On the use, usefulness, and ease of use of structural

Clack R. C., Mayer R. E., 2003, E-learning and the Science of Instruction, Jossey Bass Pfeiffer. Ma, Q., Liu, L., 2004, The technology acceptance model: a meta-analysis of empirical findings, Journal of Organizational and End User Computing, Vol.16, pp. 59–72. Masiello, I., Ramberg, R. & Lonka, K. P. O., 2005, Attitudes to the application of a Webbased learning system in a microbiology course. Computers & Education 45, pp.171-185.

Ong, C. S., Lai, J. Y. and Wang, Y. S., 1994, Factors affecting engineers' acceptance of

Raaij, E. M. V., Schepers J. J. L., 2008, The acceptance and use of a virtual learning environment in China, Computer & Education, Vol. 50, pp. 838-852.

asynchronous e-Learning in high-tech companies, Information and Management,

equation modeling in MIS research: a note of caution, MIS Quarterly, 19 (2), pp.237-

performance between e-learning teaching and the conventional teaching.

1. System style significant effects on grade and attitude towards the e-Learning. 2. The e-learning system should be based on labors' attitudes and different subjects. 3. The most efficient learning pattern will be explored in construction of working

The major findings of this chapter are as follows:

information to suit for construction is important.

Nunnally, J. C., 1978, Psychometric theory, New York: McGraw-Hill.

to the construction adoption of an e-learning system.

training.

education.

design.

**7. References** 

246.

41(6), pp.795.

come into economic knowledge. Learning for people will become more self-initiated and individualized.

Internet already starts to change the industry of education. By the characteristics of instant and without boundary, Web-Based Training (WBT) has become an important trend for the enterprises' education and training. The e-learning training mode is one of WBT and popular for enterprises. For the limitation of cost and time, e-learning has become an important trend on training, and many enterprises build an e-learning system as employees' training tool. Some companies do not know how to implement e-learning step by step or what is the factor keys to success, especially in construction fields. The critical factors of enterprise e-learning are still unclear for them.

This chapter is attempted to establish an implementation model that may help construction understand the critical factors of e-learning for effective plans. Based on e-learning, it tries to find out those critical success factors influencing over implementing and performance. Furthermore, this found that if the learning quality is increased by conducting e-learning mechanisms that can achieve the goals of reducing cost and increasing the efficiency in working training.

Since e-Learning is one of the best tools to increase value on working training, the purpose of this chapter is to understand how constructions in Taiwan implement e-learning system, what key factors to effect the adoption and processes for the implementation. In the implementation procedure, there are effective factors– course, teaching materials, instructional design, multi-media technology and infrastructure, which affect the results of achievement and learning. Therefore, this research focuses on the user's satisfaction by investigating the e-Learning mode and the training ways. However, studies of user satisfaction when using e-learning systems are very limited. This study will discuss a comprehensive model and instrument for measuring learner satisfaction with e-learning systems.

After this, we set up three groups in accordance with the result of the different learning elements on learning satisfactory investigation. During data collection and analyze, it will carefully examine evidence of reliability, content validity, criterion- related validity from the samples of a case with e-learning system. The procedures used in conceptualizing the survey, generating items, collecting data, and validating are described. To further analyze the data, statistical methods such as T-test, Correlation Analysis, and Structural Equation Modeling could be conducted.

The findings and conclusions were made based on the analyzed data and related certification Hypothesis:


e-Learning also brings advantages of flexibility and low cost for working education so that the strong strength can not be ignored. Nevertheless there are no the theoretical essentials to how to create the learning management system according to the characteristic of the enterprises. The chapter applied SEM to explore the relation among satisfaction, labor variables and system design by analyzing factors in the e-learning environment. First of all, it will analysis the system style, such as user interface, rich content, platform function, learning support. Then we had conducted an experiment to compare the learning performance between e-learning teaching and the conventional teaching.

To promote the training satisfaction and performance, this chapter highly suggests that the construction should understand learning characteristics and learning behavior from workers with safety classes to fulfill the needs from learner. Therefore, the three basic principles in conducting evaluation are based on system design (user interface, rich content, platform function, learning support), labor variables (age and gender) and satisfaction on e-learning training.

The major findings of this chapter are as follows:


According to the chapter, a successful e-learning system is integrated with the application of technology and the design of system. Therefore, this chapter will provide valuable reference to the construction adoption of an e-learning system.

#### **7. References**

56 E-Learning – Engineering, On-Job Training and Interactive Teaching

come into economic knowledge. Learning for people will become more self-initiated and

Internet already starts to change the industry of education. By the characteristics of instant and without boundary, Web-Based Training (WBT) has become an important trend for the enterprises' education and training. The e-learning training mode is one of WBT and popular for enterprises. For the limitation of cost and time, e-learning has become an important trend on training, and many enterprises build an e-learning system as employees' training tool. Some companies do not know how to implement e-learning step by step or what is the factor keys to success, especially in construction fields. The critical factors of

This chapter is attempted to establish an implementation model that may help construction understand the critical factors of e-learning for effective plans. Based on e-learning, it tries to find out those critical success factors influencing over implementing and performance. Furthermore, this found that if the learning quality is increased by conducting e-learning mechanisms that can achieve the goals of reducing cost and increasing the efficiency in

Since e-Learning is one of the best tools to increase value on working training, the purpose of this chapter is to understand how constructions in Taiwan implement e-learning system, what key factors to effect the adoption and processes for the implementation. In the implementation procedure, there are effective factors– course, teaching materials, instructional design, multi-media technology and infrastructure, which affect the results of achievement and learning. Therefore, this research focuses on the user's satisfaction by investigating the e-Learning mode and the training ways. However, studies of user satisfaction when using e-learning systems are very limited. This study will discuss a comprehensive model and instrument for measuring learner satisfaction with e-learning

After this, we set up three groups in accordance with the result of the different learning elements on learning satisfactory investigation. During data collection and analyze, it will carefully examine evidence of reliability, content validity, criterion- related validity from the samples of a case with e-learning system. The procedures used in conceptualizing the survey, generating items, collecting data, and validating are described. To further analyze the data, statistical methods such as T-test, Correlation Analysis, and Structural Equation

The findings and conclusions were made based on the analyzed data and related

2. The labors could accept the e-Learning technology that depends on age and rich

4. The exploration of satisfaction in e-learning based on different labor variables and items

3. The good system design of e-learning significant effects on grade satisfaction.

1. Different system design will influence learning motivation.

individualized.

working training.

systems.

Modeling could be conducted.

contents of e-learning.

of learning system attitudes.

certification Hypothesis:

enterprise e-learning are still unclear for them.


**5** 

*USA* 

**Facts and Fiction:** 

*DePaul University School for New Learning* 

Ruth Gannon Cook

**Lessons from Research on Faculty** 

**Motivators and Incentives to Teach Online** 

By 2012, most universities in the United States and some European universities have greatly augmented their online course offerings; those courses increasingly have been taught by adjunct (also called contingent or part-time) instructors. In view of the fact that it costs less for universities to hire adjunct instructors since they receive no benefits beyond a small salary per course, it would seem that faculty would be leaping at the opportunity to teach online and be recognized for their online contributions to offset this increased use of adjunct instructors. Indeed, according to a number of universities' chief academic officers, almost 50% of faculty accepted the value and legitimacy of online education (Allen and Seaman, 2008). Yet, almost ten years after the author's first research study on this subject (Gannon-Cook, 2003), the researcher found that faculty, both tenured and untenured, were still demurring from teaching online; she was stunned to discover that the rates of faculty teaching online remained low, particularly in the United States. During that same timeframe, ten years, there had been dramatic growth of students taking online courses (one in six students in the U.S. taking online courses as of 2006 [Pope, 2006, 1]). Data gathered from the researcher's university, as well as from a number of universities in the United States and Europe, indicated that faculty online participation percentages, despite growing numbers of online courses, averaged around twenty-five to twenty-eight percent (Ansah, and Johnson, 2003; Beggs, 2002: Bender, Wood, and Vredevoogd, 2004; Brabazon, 2001; Brookfield, 1995; Cavanaugh, 2005; Chang, 2008; Elaine and Seaman, 2006; Huffman and Miller & 2001; Jones and Johnson–Yale, 2005; Kosak, Manning, Dobson, Rogerson, Cotnam, Colaric, and McFadden, 2004;Lazarus, 2002; Lin, 2003; Maguire, 2002, 2006; Murphy, 2011; Offer, Barth, Lev, and Sheintok, 2003: O'Quinn and Corry, 2003; Paloff and Pratt, 2001; Zhen, Garthwait, & Pratt, 2008). So, a question remained that, if the numbers of online courses were burgeoning and almost 50% of faculty acknowledged the value and legitimacy of

online education, why were so few of the faculty teaching online?

The scope of this study was to look at which factors could positively motivate faculty to teach online, and in particular, to look at whether the use of adjunct faculty in the universities offering online courses affected the motivation of faculty to teach (or not teach) online courses. This study looked at data collected from thirty-eight studies of U.S. and European universities (see Appendix A) and found that, while the primary motivators for

**1. Introduction** 


## **Facts and Fiction: Lessons from Research on Faculty Motivators and Incentives to Teach Online**

Ruth Gannon Cook *DePaul University School for New Learning USA* 

#### **1. Introduction**

58 E-Learning – Engineering, On-Job Training and Interactive Teaching

Wang, K. H., Wang, T. H., Wang, W. L. & Huang, S. C., 2006, Learning styles and formative

Wilson, B. G., 1996, Constructivist Learning Environments: Case Studies in Instructional Design, Educational Technology Publication, Englewood Cliffs, New Jersey.

Journal of Computer Assisted Learning 22, pp.207-217.

assessment strategy: enhancing student achievement in Web-based learning.

By 2012, most universities in the United States and some European universities have greatly augmented their online course offerings; those courses increasingly have been taught by adjunct (also called contingent or part-time) instructors. In view of the fact that it costs less for universities to hire adjunct instructors since they receive no benefits beyond a small salary per course, it would seem that faculty would be leaping at the opportunity to teach online and be recognized for their online contributions to offset this increased use of adjunct instructors. Indeed, according to a number of universities' chief academic officers, almost 50% of faculty accepted the value and legitimacy of online education (Allen and Seaman, 2008). Yet, almost ten years after the author's first research study on this subject (Gannon-Cook, 2003), the researcher found that faculty, both tenured and untenured, were still demurring from teaching online; she was stunned to discover that the rates of faculty teaching online remained low, particularly in the United States. During that same timeframe, ten years, there had been dramatic growth of students taking online courses (one in six students in the U.S. taking online courses as of 2006 [Pope, 2006, 1]). Data gathered from the researcher's university, as well as from a number of universities in the United States and Europe, indicated that faculty online participation percentages, despite growing numbers of online courses, averaged around twenty-five to twenty-eight percent (Ansah, and Johnson, 2003; Beggs, 2002: Bender, Wood, and Vredevoogd, 2004; Brabazon, 2001; Brookfield, 1995; Cavanaugh, 2005; Chang, 2008; Elaine and Seaman, 2006; Huffman and Miller & 2001; Jones and Johnson–Yale, 2005; Kosak, Manning, Dobson, Rogerson, Cotnam, Colaric, and McFadden, 2004;Lazarus, 2002; Lin, 2003; Maguire, 2002, 2006; Murphy, 2011; Offer, Barth, Lev, and Sheintok, 2003: O'Quinn and Corry, 2003; Paloff and Pratt, 2001; Zhen, Garthwait, & Pratt, 2008). So, a question remained that, if the numbers of online courses were burgeoning and almost 50% of faculty acknowledged the value and legitimacy of online education, why were so few of the faculty teaching online?

The scope of this study was to look at which factors could positively motivate faculty to teach online, and in particular, to look at whether the use of adjunct faculty in the universities offering online courses affected the motivation of faculty to teach (or not teach) online courses. This study looked at data collected from thirty-eight studies of U.S. and European universities (see Appendix A) and found that, while the primary motivators for

Facts and Fiction:

with that data.

online courses.

Definition of Terms

**2. Review of the literature** 

2006, 2008; Zhen, Garthwait, & Pratt, 2008).

Lessons from Research on Faculty Motivators and Incentives to Teach Online 61

What remains clear, regardless of the research, is that the universities offering online courses continue to grow rapidly and the use of increasing adjunct faculty to teach those courses also continues to dramatically increase. It would also follow that if there were particular factors that could be singled out to shed light on how to enlist and retain faculty to teach online courses, then it would be worth distilling the research and providing administrators

While the terms distance education, online learning, and electronic learning (elearning) have similar meanings, there may be subtle differences ascribed to each in other research, but for

This study looked at thirty-eight studies to assess which factors could positively motivate faculty to teach online; the study also looked at whether the increased use of adjunct instructors motivated or deterred faculty from teaching online courses. The findings revealed that the primary reasons that motivated faculty to teach online were primarily based on intrinsic motivators, which should come as no surprise since most faculty enter academia motivated intrinsically to teach and help others. But later studies, after 2003, pointed to extrinsic motivators having more positive effects on influencing faculty to teach

In the studies of faculty motivation to teach online that were conducted in the early twentyfirst century, it was found that the majority of faculty choosing to teach online were motivated for largely altruistic reasons, similar to the same reasons they chose to teach (Betts, 1998; Schifter, 2000 a, b; Johnston, Alexander, Conrad, & Fieser, 2000; Gannon-Cook, 2003; Maguire, 2002; Parker, 2003; Schifter, 2000a, b; Wolcott, 1996, 2002). Of these, the two intrinsic motivators most often identified by faculty in those studies were: "ability to reach new (student) audiences that could not attend classes on campus; and, greater course flexibility for students" (Gannon-Cook, p.137). In addition to these primary motivators, early adopters also enjoyed learning new technologies and having the opportunity for personal growth through the experience of teaching online (Betts, 1998; Bower, 2002; Bruner, 2007; Chen, 2008; Gannon-Cook, 2003; Gannon-Cook, Ley, Warner, & Crawford, 2009; Johnstone, 2000; Maguire, 2002, 2006; O'Quinn, 2003; Parker, 2003; Wolcott, 2002a, 2002b,

While intrinsic motivators continued to prevail over the last ten years as the primary motivators, many of the early adopters no longer chose to teach online after a short length of time teaching online courses. The residual effects of early adoption often included faculty sharing their feedback with colleagues about their online experiences, their stories that told of many hours spent handling hundreds of emails, extra time spent answering emails, posting to discussion conferences, and helping students learn how to navigate online courses, all of which often deterred rather than encouraged other faculty to join their online teaching ranks (Betts, 1998; Bower, 2002; Johnston, Alexander, Conrad, & Fieser, 2000; O'Quinn, 2003; Southeast Missouri State University, 2001, Wolcott, 2002, 2006). For early adopters the added incentives included the opportunity to lead the cause of online teaching as role models, although, after heavy investments of time and energy teaching online, they

the purposes of this study, these terms will be used interchangeably.

faculty to teach online were intrinsic, such as the desire to make college education available to students who would not, otherwise, be able to attend college, and the desire to extend course flexibility (Betts, 1998; Bower, 2002; Bruner, 2007; Chen, 2008; Gannon-Cook, 2003; Gannon-Cook, Ley, Warner, & Crawford, 2009; Johnstone, 2000; Maguire, 2002, 2006; O'Quinn, 2003; Parker, 2003; Wolcott, 2002a,2002b, 2006, 2008; Zhen, Garthwait, & Pratt, 2008), other factors, such as increased workload for online teaching, lack of credit toward tenure and promotion, and lack of extrinsic motivators, such as stipends and course releases, deterred and demotivated as much as seventy-four percent of faculty from teaching online (Beggs, 2002; Betts, 2009; Betts, & Sikorski, 2008; Bower, 2002; Chen, 2008; Gannon-Cook, Ley, Warner, & Crawford, 2009; Johnstone, 2000; Lin, 2002; O'Quinn, 2003). Similar events were also occurring in countries, such as the "bric" countries of Brazil, Russia, India, and China, and, while some research from these countries were mentioned in this research, for the purposes of this study, none were included in the thirty-eight studies utilized herein. (Because it is estimated that the United States currently serves one-third of the world's students engaged in cross-border education (Hezel & Mitchell, 2006), the majority of this research were U. S. studies). Future studies of faculty motivators and the use of adjunct instructors in online courses within these countries can be conducted as the body of research continues to accumulate on this subject.

Findings and recommendations throughout the thirty-eight studies utilized in this research cited similar proposed remedies in order to persuade and support faculty to teach online, such as encouraging faculty voice and faculty participation in university policy, practice, online decisions, online course design and delivery, research credit towards tenure, and extrinsic motivations, such as course releases or monetary stipends. It was unclear, however, as to whether any of the recommendations from those studies were adopted by the administration of those universities (Beggs, 2002; Betts, 2009; Betts & Sikorski, 2008; Birch & Burnett, 2009; Bollinger & Wasilik, 2009; Bower, 2002; Gannon-Cook, 2003; McLean, 2006a,b;O'Quinn, 2003, 2004; O'Quinn, & Corry, 2002; Panda & Mishra, 2008; Quinn, Schifter, 2000a,b; Schifter, 2002; Soldner, Lee, Duby, 2004; Trower, 2008; Wolcott, 2006).

Another important research question that needed to be asked (and was asked in a number of the thirty-eight studies) was whether the universities that used large percentages of adjunct instructors for online courses found any significant differences in student retention rates in those courses. While the increased use of adjuncts was widely acknowledged, this factor was seldom mentioned as a factor in studies about faculty motivation (or demotivation) to teach online courses, so the opportunity to look at a number of studies addressing faculty motivation would provide some insights and inferences that could be made for how to best motivate faculty to teach online. Moreover, since much of the research has not focused on whether faculty teaching online brings higher student retention and completion rates than adjunct instructors, this factor is also addressed in this study.

If the research does document higher student retention with full-time faculty instructors as opposed to adjunct faculty instructors, as the studies utilized in this research seem to indicate (American Association of University Professors, 2003; Ansah & Johnson, 2003; Benjamin, 2002; Chapman, 2011; McArthur, 1999; Schibik & Harrington, 2004; Southern Area Southern Association of Colleges and Schools, 2010; Xenos, Pierrakaes, C., & Pintelas, 2002), then it might be productive to review the recommendations of those studies to see which factors would work best to best secure and retain full-time faculty to teach online courses.

What remains clear, regardless of the research, is that the universities offering online courses continue to grow rapidly and the use of increasing adjunct faculty to teach those courses also continues to dramatically increase. It would also follow that if there were particular factors that could be singled out to shed light on how to enlist and retain faculty to teach online courses, then it would be worth distilling the research and providing administrators with that data.

#### Definition of Terms

60 E-Learning – Engineering, On-Job Training and Interactive Teaching

faculty to teach online were intrinsic, such as the desire to make college education available to students who would not, otherwise, be able to attend college, and the desire to extend course flexibility (Betts, 1998; Bower, 2002; Bruner, 2007; Chen, 2008; Gannon-Cook, 2003; Gannon-Cook, Ley, Warner, & Crawford, 2009; Johnstone, 2000; Maguire, 2002, 2006; O'Quinn, 2003; Parker, 2003; Wolcott, 2002a,2002b, 2006, 2008; Zhen, Garthwait, & Pratt, 2008), other factors, such as increased workload for online teaching, lack of credit toward tenure and promotion, and lack of extrinsic motivators, such as stipends and course releases, deterred and demotivated as much as seventy-four percent of faculty from teaching online (Beggs, 2002; Betts, 2009; Betts, & Sikorski, 2008; Bower, 2002; Chen, 2008; Gannon-Cook, Ley, Warner, & Crawford, 2009; Johnstone, 2000; Lin, 2002; O'Quinn, 2003). Similar events were also occurring in countries, such as the "bric" countries of Brazil, Russia, India, and China, and, while some research from these countries were mentioned in this research, for the purposes of this study, none were included in the thirty-eight studies utilized herein. (Because it is estimated that the United States currently serves one-third of the world's students engaged in cross-border education (Hezel & Mitchell, 2006), the majority of this research were U. S. studies). Future studies of faculty motivators and the use of adjunct instructors in online courses within these countries can be conducted as the body of research

Findings and recommendations throughout the thirty-eight studies utilized in this research cited similar proposed remedies in order to persuade and support faculty to teach online, such as encouraging faculty voice and faculty participation in university policy, practice, online decisions, online course design and delivery, research credit towards tenure, and extrinsic motivations, such as course releases or monetary stipends. It was unclear, however, as to whether any of the recommendations from those studies were adopted by the administration of those universities (Beggs, 2002; Betts, 2009; Betts & Sikorski, 2008; Birch & Burnett, 2009; Bollinger & Wasilik, 2009; Bower, 2002; Gannon-Cook, 2003; McLean, 2006a,b;O'Quinn, 2003, 2004; O'Quinn, & Corry, 2002; Panda & Mishra, 2008; Quinn, Schifter, 2000a,b; Schifter, 2002; Soldner, Lee, Duby, 2004; Trower, 2008; Wolcott, 2006).

Another important research question that needed to be asked (and was asked in a number of the thirty-eight studies) was whether the universities that used large percentages of adjunct instructors for online courses found any significant differences in student retention rates in those courses. While the increased use of adjuncts was widely acknowledged, this factor was seldom mentioned as a factor in studies about faculty motivation (or demotivation) to teach online courses, so the opportunity to look at a number of studies addressing faculty motivation would provide some insights and inferences that could be made for how to best motivate faculty to teach online. Moreover, since much of the research has not focused on whether faculty teaching online brings higher student retention and completion rates than

If the research does document higher student retention with full-time faculty instructors as opposed to adjunct faculty instructors, as the studies utilized in this research seem to indicate (American Association of University Professors, 2003; Ansah & Johnson, 2003; Benjamin, 2002; Chapman, 2011; McArthur, 1999; Schibik & Harrington, 2004; Southern Area Southern Association of Colleges and Schools, 2010; Xenos, Pierrakaes, C., & Pintelas, 2002), then it might be productive to review the recommendations of those studies to see which factors would work best to best secure and retain full-time faculty to teach online

adjunct instructors, this factor is also addressed in this study.

courses.

continues to accumulate on this subject.

While the terms distance education, online learning, and electronic learning (elearning) have similar meanings, there may be subtle differences ascribed to each in other research, but for the purposes of this study, these terms will be used interchangeably.

#### **2. Review of the literature**

This study looked at thirty-eight studies to assess which factors could positively motivate faculty to teach online; the study also looked at whether the increased use of adjunct instructors motivated or deterred faculty from teaching online courses. The findings revealed that the primary reasons that motivated faculty to teach online were primarily based on intrinsic motivators, which should come as no surprise since most faculty enter academia motivated intrinsically to teach and help others. But later studies, after 2003, pointed to extrinsic motivators having more positive effects on influencing faculty to teach online courses.

In the studies of faculty motivation to teach online that were conducted in the early twentyfirst century, it was found that the majority of faculty choosing to teach online were motivated for largely altruistic reasons, similar to the same reasons they chose to teach (Betts, 1998; Schifter, 2000 a, b; Johnston, Alexander, Conrad, & Fieser, 2000; Gannon-Cook, 2003; Maguire, 2002; Parker, 2003; Schifter, 2000a, b; Wolcott, 1996, 2002). Of these, the two intrinsic motivators most often identified by faculty in those studies were: "ability to reach new (student) audiences that could not attend classes on campus; and, greater course flexibility for students" (Gannon-Cook, p.137). In addition to these primary motivators, early adopters also enjoyed learning new technologies and having the opportunity for personal growth through the experience of teaching online (Betts, 1998; Bower, 2002; Bruner, 2007; Chen, 2008; Gannon-Cook, 2003; Gannon-Cook, Ley, Warner, & Crawford, 2009; Johnstone, 2000; Maguire, 2002, 2006; O'Quinn, 2003; Parker, 2003; Wolcott, 2002a, 2002b, 2006, 2008; Zhen, Garthwait, & Pratt, 2008).

While intrinsic motivators continued to prevail over the last ten years as the primary motivators, many of the early adopters no longer chose to teach online after a short length of time teaching online courses. The residual effects of early adoption often included faculty sharing their feedback with colleagues about their online experiences, their stories that told of many hours spent handling hundreds of emails, extra time spent answering emails, posting to discussion conferences, and helping students learn how to navigate online courses, all of which often deterred rather than encouraged other faculty to join their online teaching ranks (Betts, 1998; Bower, 2002; Johnston, Alexander, Conrad, & Fieser, 2000; O'Quinn, 2003; Southeast Missouri State University, 2001, Wolcott, 2002, 2006). For early adopters the added incentives included the opportunity to lead the cause of online teaching as role models, although, after heavy investments of time and energy teaching online, they

Facts and Fiction:

online.

2002; Parker, 2003; Wolcott, 2006).

Lessons from Research on Faculty Motivators and Incentives to Teach Online 63

that must still be met, such as rent, and food (Gannon-Cook, 2010). When faced with the increased demands of teaching online for their home universities, several studies even indicated that there were faculty choosing to teach online for other universities instead of teaching online for their home universities (where they were employed full-time) because there was no additional compensation for teaching online at their home universities (Bowers, 2002; Gannon-Cook, 2003; Johnston, Alexander, Conrad, & Fieser, 2000, Maguire,

The use of increasing adjunct faculty to teach online courses will also continue, largely because there is such a strong need for instructors for online courses and programs in the rapidly expanding world of virtual education. But there are other reasons too for this trend that are more bottom line: adjuncts do not require benefits, such as insurance, and adjuncts can be paid far less than faculty. With these savings apparent to university administrators, there is often a rush to the use of adjunct instructors in online courses, and in fact, the trend to hire more or all adjunct instructors is the case of many for-profit universities. Without a thorough investigation, there could be a lack of understanding by administration as to the costs and benefits of enlisting adjunct instructors rather than full-time faculty to teach

High student attrition in online courses and programs (average 40%-60%) (Betts & Sikorski, 2008; Gannon-Cook, 2003; United States Distance Learning Association, 2001, 2007) could end up costing universities more than the costs associated with employing full-time faculty. A look at what student attrition costs universities would be worth the time invested, particularly since online attrition rates (in the United States) range from 40% to 50%, and can be as high as 70% to 80% (Betts & Sikorski, 2008; National Center of Educational Statistics, 2010, 2011). Attrition can be even higher in online programs, often losing 40-60% of first year students (Betts, 2008). Further research into student retentions and course completions might provide alternative administrative solutions that reap greater financial benefits. Even the retention of one or two students in each online course could add up to a cumulative effect in the overall retention rates of students which, in turn, could also have significant bottom line ramifications financially for the university in both course and degree completions (National Center of Educational Statistics, 2010, 2011). According to the National Center of Educational Statistics (2010, 2011), if university retention rates in online courses and programs could be raised to the average 78.6% retention rate reported for traditional universities, or even if the financial bottom line could be improved by five to eight percent in revenues with these retention increases, this could translate to several

These numbers could be very compelling to administrators looking for ways to increase revenues and retain students and worth further investigation at their universities. The American Association of University Professors (AAUP), an organization co-founded by John

The dramatic increase in the number and proportion of contingent faculty in the last ten years has created systemic problems for higher education. Student learning is diminished by reduced contact with tenured faculty members, whose expertise in their field and effectiveness as teachers have been validated by peer review and to whom the institution has made a long-term commitment. Faculty governance is weakened by constant turnover

Dewey to provide a criterion of quality higher education academic standards, cites.

million dollars over a several-year period for the university.

often did not often remain teaching online, and in some cases, actually discouraged other faculty from teaching online (Bower, 2002; Chang, 2007; Culp, Riffee, Starrett, Sarin, & Abrahansen, 2001; Distance Education Report, 2001; Jacobsen, 2000; Jones, Johnson-Yale, 2005; Lazarus, 2003; Lin, 2002; Kosak, Manning, Dobson, Rogerson, Cotnam, Colaric, & McFadden, 2004;Maguire 2002; O'Quinn & Corey, 2002; Paloff & Pratt, 2001; Wolcott, 2002a; Zhen, Garthwait, & Pratt, 2008).

There were also other factors that were demotivating and deterred faculty from teaching online. In some cases teaching online actually posed real threats to faculty quests for tenure, factors such as the increased workload involved in online teaching, the lack of credit toward tenure and promotion for online teaching, and the lack of other incentives, such as raises, or course stipends. So, while administration in many universities touted as much as fifty percent of faculty being interested in teaching online, in truth, as much as seventy percent of faculty in the studies still declined to teach online (Beggs, 2002; Betts, & Sikorski, 2008; Bower, 2002; Chen, 2008; Gannon-Cook, Ley, Warner, & Crawford, 2009; Johnstone, 2000; Lin, 2002; Murphy, 2011; O'Quinn, 2003; Parker, 2003).

The recommendations throughout these studies repeatedly cited remedies to provide faculty with incentives to teach online that did not involve large investments of monies, such as the encouragement of faculty voice, the inclusion of faculty in university policy and practice decisions, and the awarding of online teaching credit towards tenure (Ansah, & Johnson, 2003); Betts, 1998; Betts, 2008; French, 2001; Gannon-Cook, Ley, Warner, & Crawford, 2009; Maguire, 2006; Schifter, 2000a,b, 2002; Soldner, Lee, Duby, 2004; Wolcott, 1996, 2002a,b; Zhen, Garthwait,& Pratt, 2008). The recommendations also included the awarding of extrinsic motivators that would incur increased administrative costs, such as course releases or monetary stipends, but could also prove to be a profitable investment if it resulted in higher student retention and online course completion rates (Betts & Sikorski, 2008; Betts, 2009; Bollinger & Wasilik, 2009; Johnston, Alexander, Conrad, & Fieser, 2000; Maguire, 2002, 2006; McLean,2006b; O'Quinn, 2002, 2003; Quinn, 2008; Parker, 2003; Wolcott, 2006, 2008; Zhen, Garthwait, & Pratt, 2008).

Some of the factors that received the largest number of responses from faculty in those thirty-eight studies were extrinsic, and those factors would provide distinct benefits to faculty teaching online courses. So, while intrinsic factors may have first inspired participation in online teaching, research studies, such as those conducted by Beggs (2002), Bowers (2002), Gannon-Cook (2003), Stevens (2001), and Wolcott (2002a,b), reported that faculty participating in continued online teaching were often supported by extrinsic factors, such as increased salary, course releases, and credit toward tenure.

Lack of incentives has become an increasing barrier to institutional growth in offering distance education. Studies, such as that of the Distance Education Report (2001), Southeast Missouri State University (2001), Zhen, Garthwait, & Pratt (2008), found that issues related to faculty were of greater relevance to faculty teaching online than technological issues. While technological issues could become a concern, they were often not elevated to a level of anxiety by faculty members until they had taught online at least one or more times. But once faculty members began teaching online, they often assessed the time invested and, despite their commitment to be of help to the students, started to take inventory of the time demands required to teach online courses. Since salaries often do not keep pace with the rising costs of living, these faculty members were often faced with basic physiological needs

often did not often remain teaching online, and in some cases, actually discouraged other faculty from teaching online (Bower, 2002; Chang, 2007; Culp, Riffee, Starrett, Sarin, & Abrahansen, 2001; Distance Education Report, 2001; Jacobsen, 2000; Jones, Johnson-Yale, 2005; Lazarus, 2003; Lin, 2002; Kosak, Manning, Dobson, Rogerson, Cotnam, Colaric, & McFadden, 2004;Maguire 2002; O'Quinn & Corey, 2002; Paloff & Pratt, 2001; Wolcott, 2002a;

There were also other factors that were demotivating and deterred faculty from teaching online. In some cases teaching online actually posed real threats to faculty quests for tenure, factors such as the increased workload involved in online teaching, the lack of credit toward tenure and promotion for online teaching, and the lack of other incentives, such as raises, or course stipends. So, while administration in many universities touted as much as fifty percent of faculty being interested in teaching online, in truth, as much as seventy percent of faculty in the studies still declined to teach online (Beggs, 2002; Betts, & Sikorski, 2008; Bower, 2002; Chen, 2008; Gannon-Cook, Ley, Warner, & Crawford, 2009; Johnstone, 2000;

The recommendations throughout these studies repeatedly cited remedies to provide faculty with incentives to teach online that did not involve large investments of monies, such as the encouragement of faculty voice, the inclusion of faculty in university policy and practice decisions, and the awarding of online teaching credit towards tenure (Ansah, & Johnson, 2003); Betts, 1998; Betts, 2008; French, 2001; Gannon-Cook, Ley, Warner, & Crawford, 2009; Maguire, 2006; Schifter, 2000a,b, 2002; Soldner, Lee, Duby, 2004; Wolcott, 1996, 2002a,b; Zhen, Garthwait,& Pratt, 2008). The recommendations also included the awarding of extrinsic motivators that would incur increased administrative costs, such as course releases or monetary stipends, but could also prove to be a profitable investment if it resulted in higher student retention and online course completion rates (Betts & Sikorski, 2008; Betts, 2009; Bollinger & Wasilik, 2009; Johnston, Alexander, Conrad, & Fieser, 2000; Maguire, 2002, 2006; McLean,2006b; O'Quinn, 2002, 2003; Quinn, 2008; Parker, 2003;

Some of the factors that received the largest number of responses from faculty in those thirty-eight studies were extrinsic, and those factors would provide distinct benefits to faculty teaching online courses. So, while intrinsic factors may have first inspired participation in online teaching, research studies, such as those conducted by Beggs (2002), Bowers (2002), Gannon-Cook (2003), Stevens (2001), and Wolcott (2002a,b), reported that faculty participating in continued online teaching were often supported by extrinsic factors,

Lack of incentives has become an increasing barrier to institutional growth in offering distance education. Studies, such as that of the Distance Education Report (2001), Southeast Missouri State University (2001), Zhen, Garthwait, & Pratt (2008), found that issues related to faculty were of greater relevance to faculty teaching online than technological issues. While technological issues could become a concern, they were often not elevated to a level of anxiety by faculty members until they had taught online at least one or more times. But once faculty members began teaching online, they often assessed the time invested and, despite their commitment to be of help to the students, started to take inventory of the time demands required to teach online courses. Since salaries often do not keep pace with the rising costs of living, these faculty members were often faced with basic physiological needs

Zhen, Garthwait, & Pratt, 2008).

Lin, 2002; Murphy, 2011; O'Quinn, 2003; Parker, 2003).

Wolcott, 2006, 2008; Zhen, Garthwait, & Pratt, 2008).

such as increased salary, course releases, and credit toward tenure.

that must still be met, such as rent, and food (Gannon-Cook, 2010). When faced with the increased demands of teaching online for their home universities, several studies even indicated that there were faculty choosing to teach online for other universities instead of teaching online for their home universities (where they were employed full-time) because there was no additional compensation for teaching online at their home universities (Bowers, 2002; Gannon-Cook, 2003; Johnston, Alexander, Conrad, & Fieser, 2000, Maguire, 2002; Parker, 2003; Wolcott, 2006).

The use of increasing adjunct faculty to teach online courses will also continue, largely because there is such a strong need for instructors for online courses and programs in the rapidly expanding world of virtual education. But there are other reasons too for this trend that are more bottom line: adjuncts do not require benefits, such as insurance, and adjuncts can be paid far less than faculty. With these savings apparent to university administrators, there is often a rush to the use of adjunct instructors in online courses, and in fact, the trend to hire more or all adjunct instructors is the case of many for-profit universities. Without a thorough investigation, there could be a lack of understanding by administration as to the costs and benefits of enlisting adjunct instructors rather than full-time faculty to teach online.

High student attrition in online courses and programs (average 40%-60%) (Betts & Sikorski, 2008; Gannon-Cook, 2003; United States Distance Learning Association, 2001, 2007) could end up costing universities more than the costs associated with employing full-time faculty. A look at what student attrition costs universities would be worth the time invested, particularly since online attrition rates (in the United States) range from 40% to 50%, and can be as high as 70% to 80% (Betts & Sikorski, 2008; National Center of Educational Statistics, 2010, 2011). Attrition can be even higher in online programs, often losing 40-60% of first year students (Betts, 2008). Further research into student retentions and course completions might provide alternative administrative solutions that reap greater financial benefits. Even the retention of one or two students in each online course could add up to a cumulative effect in the overall retention rates of students which, in turn, could also have significant bottom line ramifications financially for the university in both course and degree completions (National Center of Educational Statistics, 2010, 2011). According to the National Center of Educational Statistics (2010, 2011), if university retention rates in online courses and programs could be raised to the average 78.6% retention rate reported for traditional universities, or even if the financial bottom line could be improved by five to eight percent in revenues with these retention increases, this could translate to several million dollars over a several-year period for the university.

These numbers could be very compelling to administrators looking for ways to increase revenues and retain students and worth further investigation at their universities. The American Association of University Professors (AAUP), an organization co-founded by John Dewey to provide a criterion of quality higher education academic standards, cites.

The dramatic increase in the number and proportion of contingent faculty in the last ten years has created systemic problems for higher education. Student learning is diminished by reduced contact with tenured faculty members, whose expertise in their field and effectiveness as teachers have been validated by peer review and to whom the institution has made a long-term commitment. Faculty governance is weakened by constant turnover

Facts and Fiction:

listed the factors as:

lack of credit or promotion; lack of recognition and award;

concern about faculty workload; lack of technical support; lack of support from colleagues;

lack of support from Dean and university administrators

reticent to teach online; the two intrinsic factors most often cited were:

lack of salary; lack of merit pay;

lack of royalties; lack of release time;

online courses

teaching online.

Crawford, Warner, 2009; Wolcott, 2002ab, 2006).

Lessons from Research on Faculty Motivators and Incentives to Teach Online 65

motivators in a number of studies were reduced teaching load or release time (Betts, 1998, 2009; Bower, 2002; Schifter, 2000a,b; Gannon-Cook, 2003; Johnston, Alexander, Conrad, & Fieser, 2000; Wolcott 2002a, 2002b); faculty training (Bebko, 1998; Beggs, 2002; Betts, 2009; Clarke, Butler, Schmidt-Hansen, & Somerville, 2004; Culp, Riffee, Starrett, Sarin, & Abrahansen, 2001; Donovan, 2004; Edwards, 2004; French, 2001; Lin, 2002; Twigg, 2000); and money, such as stipends, raises, or additional payments for teaching online (Betts, & Sikorski, 2008; Bower, 2002, Bruner, 2007; Gannon-Cook, 2003,2009; Gannon-Cook, Ley,

But there were also studies that looked at negative motivators to teaching online: (Akbulut, Kuzu, Latchem, Odabasi, 2007; begs, 2002; Betts, 1998; Bower, 2002; Bruner, 2007; Gannon-Cook, 2003; Johnston, Alexander, Conrad, & Fieser, 2000; Kwoumka & Gannon Cook, 2003: Lin, 2002; O'Quinn, 2003: Parker, 2003; Schifter, 2002; Twigg, 2000; Wolcott, 2002a, b). The studies' findings suggested that there were factors that could put off faculty from even considering teaching online (Beggs, 2002; Betts, 1998; Bruner, 2007; Gannon-Cook, Ley, Crawford, Warner, 2009; Maguire, 2002, 2006; O'Quinn, & Corry, 2002; Schifter, 2000a, b; West, Waddoups, & Graham, 2007; Wolcott, 1996, 2002a, b); each study contained similar findings, but there were slight variances in the factor rankings. The majority of the studies

Many of those studies also cited several intrinsic deterrents that were voiced by faculty

concern over the quality of instruction for online students; and concern over the quality of

Since each study's findings related to the university where that study was conducted, findings could not be generalized, but the repeated mention of the above-listed deterrents, both extrinsic and intrinsic, throughout the studies, pointed to the strong likelihood that these factors represented at least a representative number of faculty members' feelings about

While the findings mentioned above were the most cited in the studies researched herein, there were also some surprising findings that surfaced in a number of studies that might also be worth looking at when considering which factors would best motivate faculty to teach online. There were other factors that were not singled out as meaningful to faculty, but that may have been because the factors were not understood by faculty who had not previously taught online. Perhaps the reasons may have been that those faculty were just becoming aware of the potential time commitments required for online teaching (faculty

and, on many campuses, by the exclusion of contingent faculty from governance activities. (American Association of University Professors, 2011, p.2)

While there are other factors that contribute to student attrition in online courses, such as family responsibilities, job requirements, and other personal factors, attrition can still be reduced with careful attention, such as advising, mentoring, and nurturing, and all these can generate university allegiance, but do prove more challenging for universities without strong cores of full-time faculty (Allen & Seaman, 2010; Betts, 2008; Howell, Laws, & Lindsay, 2004; Southern Area Southern Association of Colleges and Schools, 2010). Interactivities beyond discussion boards and drop boxes can create bonds based on "human interaction fostered through instruction, programming, and personalized engagement" (Betts, 2008, p.399). Adjunct instructors can do these kinds of interactivities, but, like their students, they too have other responsibilities, such as full time jobs elsewhere, or a multiple of universities where they teach online. So, while they may teach effectively, they don't have time to get to know their part-time employers' university cultures, nor do they have much time to mentor and nurture, or generate university allegiance in their students when they don't have that allegiance themselves. Full time faculty members' careers revolve around not only teaching, but service and research, and they can nurture their students with consistency and follow through with their students that is less possible with adjunct instructors. There are some studies that look at whether the consistency of full-time faculty teaching online courses increases student retention and completion rates in online courses (Benjamin, 2002; Betts & Sikorski, 2008; Bower, 2002; Bruner, 2007; Chapman,2011 Gannon-Cook, 2003; Johnston, Alexander, Conrad, & Fieser, 2000, Luzer, 2011; Maguire, 2002; Wolcott, 2006]). To-date, however, the trend towards the use of adjuncts in teaching online courses continues and so does the growing argument in many universities against full time faculty.

In the United States and in the European countries, the standards established by accrediting agencies like the Southern Association of Colleges and Schools (2003), and by organizations, like the American Association of University Professors (2010), state the university should maintain a parity of adjunct instructors to full-time faculty. For example the Southern Association of Colleges and Schools (2010), which monitor accreditation standards for most southern states in the United States, requires a parity of no more than one full-time faculty to four adjunct instructors; moreover, it also mandates that "at least 25 percent of the discipline course hours in each major at the baccalaureate level (must be) taught by faculty members holding the terminal degree" (p.28). Due to the high demand for online instructors, it may become increasingly more difficult to maintain this parity without enlisting more university faculty to teach online or disregarding accrediting agency guidelines, and this again raises the question of how faculty can be motivated to teach these growing numbers of online courses.

While a number of the studies still maintained a need to intrinsically motivate faculty with "atta boys" (verbal or written compliments) for their teaching online (Ansah & Johnson, 2003; Beggs, 2002; Bruner, 2007; Cavanaugh, 2005; Jacobsen, 2000; O'Quinn, 2003; Schifter, 2000a, b; Wolcott, 2006; Zhen, Garthwait, & Pratt, 2008), studies conducted in the last seven eight years have pointed to the need for extrinsic motivators to enlist and keep faculty involved in teaching online. Among those studies recommending extrinsic motivators, there was some argument about which incentives worked best, but the primary extrinsic

and, on many campuses, by the exclusion of contingent faculty from governance activities.

While there are other factors that contribute to student attrition in online courses, such as family responsibilities, job requirements, and other personal factors, attrition can still be reduced with careful attention, such as advising, mentoring, and nurturing, and all these can generate university allegiance, but do prove more challenging for universities without strong cores of full-time faculty (Allen & Seaman, 2010; Betts, 2008; Howell, Laws, & Lindsay, 2004; Southern Area Southern Association of Colleges and Schools, 2010). Interactivities beyond discussion boards and drop boxes can create bonds based on "human interaction fostered through instruction, programming, and personalized engagement" (Betts, 2008, p.399). Adjunct instructors can do these kinds of interactivities, but, like their students, they too have other responsibilities, such as full time jobs elsewhere, or a multiple of universities where they teach online. So, while they may teach effectively, they don't have time to get to know their part-time employers' university cultures, nor do they have much time to mentor and nurture, or generate university allegiance in their students when they don't have that allegiance themselves. Full time faculty members' careers revolve around not only teaching, but service and research, and they can nurture their students with consistency and follow through with their students that is less possible with adjunct instructors. There are some studies that look at whether the consistency of full-time faculty teaching online courses increases student retention and completion rates in online courses (Benjamin, 2002; Betts & Sikorski, 2008; Bower, 2002; Bruner, 2007; Chapman,2011 Gannon-Cook, 2003; Johnston, Alexander, Conrad, & Fieser, 2000, Luzer, 2011; Maguire, 2002; Wolcott, 2006]). To-date, however, the trend towards the use of adjuncts in teaching online courses continues and so does the growing argument in many universities against full time

In the United States and in the European countries, the standards established by accrediting agencies like the Southern Association of Colleges and Schools (2003), and by organizations, like the American Association of University Professors (2010), state the university should maintain a parity of adjunct instructors to full-time faculty. For example the Southern Association of Colleges and Schools (2010), which monitor accreditation standards for most southern states in the United States, requires a parity of no more than one full-time faculty to four adjunct instructors; moreover, it also mandates that "at least 25 percent of the discipline course hours in each major at the baccalaureate level (must be) taught by faculty members holding the terminal degree" (p.28). Due to the high demand for online instructors, it may become increasingly more difficult to maintain this parity without enlisting more university faculty to teach online or disregarding accrediting agency guidelines, and this again raises the question of how faculty can be motivated to teach these

While a number of the studies still maintained a need to intrinsically motivate faculty with "atta boys" (verbal or written compliments) for their teaching online (Ansah & Johnson, 2003; Beggs, 2002; Bruner, 2007; Cavanaugh, 2005; Jacobsen, 2000; O'Quinn, 2003; Schifter, 2000a, b; Wolcott, 2006; Zhen, Garthwait, & Pratt, 2008), studies conducted in the last seven eight years have pointed to the need for extrinsic motivators to enlist and keep faculty involved in teaching online. Among those studies recommending extrinsic motivators, there was some argument about which incentives worked best, but the primary extrinsic

(American Association of University Professors, 2011, p.2)

faculty.

growing numbers of online courses.

motivators in a number of studies were reduced teaching load or release time (Betts, 1998, 2009; Bower, 2002; Schifter, 2000a,b; Gannon-Cook, 2003; Johnston, Alexander, Conrad, & Fieser, 2000; Wolcott 2002a, 2002b); faculty training (Bebko, 1998; Beggs, 2002; Betts, 2009; Clarke, Butler, Schmidt-Hansen, & Somerville, 2004; Culp, Riffee, Starrett, Sarin, & Abrahansen, 2001; Donovan, 2004; Edwards, 2004; French, 2001; Lin, 2002; Twigg, 2000); and money, such as stipends, raises, or additional payments for teaching online (Betts, & Sikorski, 2008; Bower, 2002, Bruner, 2007; Gannon-Cook, 2003,2009; Gannon-Cook, Ley, Crawford, Warner, 2009; Wolcott, 2002ab, 2006).

But there were also studies that looked at negative motivators to teaching online: (Akbulut, Kuzu, Latchem, Odabasi, 2007; begs, 2002; Betts, 1998; Bower, 2002; Bruner, 2007; Gannon-Cook, 2003; Johnston, Alexander, Conrad, & Fieser, 2000; Kwoumka & Gannon Cook, 2003: Lin, 2002; O'Quinn, 2003: Parker, 2003; Schifter, 2002; Twigg, 2000; Wolcott, 2002a, b). The studies' findings suggested that there were factors that could put off faculty from even considering teaching online (Beggs, 2002; Betts, 1998; Bruner, 2007; Gannon-Cook, Ley, Crawford, Warner, 2009; Maguire, 2002, 2006; O'Quinn, & Corry, 2002; Schifter, 2000a, b; West, Waddoups, & Graham, 2007; Wolcott, 1996, 2002a, b); each study contained similar findings, but there were slight variances in the factor rankings. The majority of the studies listed the factors as:

lack of salary; lack of merit pay; lack of credit or promotion; lack of recognition and award; lack of royalties; lack of release time; concern about faculty workload; lack of technical support; lack of support from colleagues; lack of support from Dean and university administrators

Many of those studies also cited several intrinsic deterrents that were voiced by faculty reticent to teach online; the two intrinsic factors most often cited were:

concern over the quality of instruction for online students; and concern over the quality of online courses

Since each study's findings related to the university where that study was conducted, findings could not be generalized, but the repeated mention of the above-listed deterrents, both extrinsic and intrinsic, throughout the studies, pointed to the strong likelihood that these factors represented at least a representative number of faculty members' feelings about teaching online.

While the findings mentioned above were the most cited in the studies researched herein, there were also some surprising findings that surfaced in a number of studies that might also be worth looking at when considering which factors would best motivate faculty to teach online. There were other factors that were not singled out as meaningful to faculty, but that may have been because the factors were not understood by faculty who had not previously taught online. Perhaps the reasons may have been that those faculty were just becoming aware of the potential time commitments required for online teaching (faculty

Facts and Fiction:

teaching online.

teach online courses.

what they already have developed elsewhere.

throughout the major Brazilian universities over the next decade.

Lessons from Research on Faculty Motivators and Incentives to Teach Online 67

providing positive administrative support. In some comments generated from faculty it seemed that a number of respondents felt that faculty should be included in university decisions, particularly with respect to curricular and design decisions that affected both faculty and students. Other considerations, such as credit toward tenure, could also fall under the category of "support" and would also acknowledge the work involved in

This study did not look at trends in other countries, so research will need to be conducted to see if the recommendations from studies, such as the ones cited herein, would be applicable to universities offering online courses and programs in those countries. For universities seeking to provide cross-border education, it would make sense to consider the cultures and traditions of those countries first and implement measures that honor those priorities, then conduct studies, similar to the ones cited here, that address the use of adjuncts and full-time faculty, and the bottom-line cost comparisons of using more adjuncts or full-time faculty to

One Taiwanese study of online students pointed to a lack of adequate mentoring or advice from experienced and knowledgeable faculty as one of the most frequently cited sources of delay in completing their degrees (Kuo, 2011). It was also mentioned in several Chinese studies (Chang, Martin, Schellens, 2010; Huang, Dedegikas, Walls,2009; Zhang, Zhang, Duan, Fu, Wang, 2010) that it was important for the university to be aligned with its mission and that "Chinese students are used to the classroom teaching style. The reason for this situation could be that the traditional face-to-face class is still the main style of teaching in China" (Khou, 2011, p.6). Questions were also raised about whether the universities studied articulated global learning as a goal for its undergraduates; there were also questions about how faculty were rewarded for their teaching and scholarship (Chapman, 2011) and there were comments that cross-border providers may not meet each country's priorities, and perhaps these locales are better served by their local universities than by foreign providers that more frequently deliver

A review of research on Brazilian universities (Abrahão & Malanga, 2010) revealed universities have been undergoing a turning point in Brazil's brief history of higher education (the first Brazilian university, the University of Sao Paulo, was only founded in 1934). Therefore, the analysis has evidenced that "public policies for higher education must signal if they want to provide professional teaching in higher education, geared toward the job market, or rather, teaching from a real university, thus basing its mission, ethos and episteme in the 21st century on knowledge production and transmission" (p.43). No doubt if the choice is the latter, then there will be a strong need for full-time tenure-track and tenured faculty. While Brazil is also turning more to online education, it appears that there will be an emphasis placed on a "real university" experience which may necessitate traditional university standards and faculty, and will include the enlistment of faculty to teach online, so motivating faculty will become more important as the universities continue to grow. Future studies may reveal the progress of online courses and degree programs

In summary, there were a number of factors cited in the thirty-eight studies that could enlist and retain faculty to teach online courses and also allow the universities to best focus on their student populations and financial needs. If some of these recommendations have been adopted in the universities studied, then perhaps there can be follow-up studies that can

who had previously taught online were quick to share their online teaching experiences with fellow faculty members, but there still seemed to be a general lack of awareness of the time involved in teaching online classes). In addition, little or no evidence may have been available to the faculty participating in these studies that would provide insights into time required to create the content and deliver course materials in online environments, or the time to work with the instructional designer or design teams that would co-create the online courses with the faculty serving as content matter expert for the online course(s). The steps involved in designing, implementing, and teaching online extended beyond traditional inclass preparation time, largely because online courses required more online interactivities, more graphic representations, and more attention to cultural implications, all while there were additional considerations, such as how the course materials would be housed within the learning management system, and how the technology would be utilized inside and outside the course (Ansah, & Johnson, 2003; Betts, 1998; Bender, Wood, & Vredevoogd, 2004; Cavanaugh, 2005; Chang, 2007; Chang 2008; French, 2001; Harley, 2002; Jacobsen, 2000; Lazarus, 2003; Weller, 2006).

A surprising finding was how low the ranking was over the factor of copyright ownership rights, and in some studies this factor was not even a consideration by faculty who had participated in the studies. Few faculty seemed to be concerned over copyright ownership rights, or royalties, at least as of the latest studies reviewed for this research (Betts, 2009; Gannon-Cook, Ley, Warner, & Crawford, 2010; Quinn & Trower, 2008; Wolcott, 2006; Zhen, Garthwait, & Pratt, 2008). (Concern over royalties was listed as one of the deterrents to faculty participating online in a number of the studies used in this research, but this factor ranked at most, fourth on the list of factors that could affect participation in online courses (Gannon-Cook, Ley, Crawford, Warner, 2009; Maguire, 2002, 2006; O'Quinn, & Corry, 2002; Schifter, 2000a, b; Wolcott, 1996, 2002a, b). Part of the reason for the lack of faculty interest or concern about ownership or royalties may have been because many faculty usually designed their own on-ground (traditional classroom) classes as a part of their teaching duties for the university; they did not generally copyright their courses, nor did they receive any royalties, or additional compensation for their courses. So it would follow that faculty that had not taught online would not think about copyrights or the potential problems that could surface with respect to contracts for designing courses that could either include payments for the design of courses and royalties, or "work for hire" courses designed for the university without any royalty rights. Without faculty being presented with the pros and cons of online course contracts, it would make sense that faculty who had not taught online previously would not pay attention to this factor when responding to surveys requesting their prioritizing of copyright considerations in designing online courses.

Another factor that was mentioned in many studies (Beggs, 2002; Betts, 1998, 2008; Bollinger & Wasilik, 2009; Bower, 2002; O'Quinn, 2003; O'Quinn & Corry, 2002; Quinn & Trower, 2008; Wolcott, 2006; Zhen & Pratt, 2008) was "lack of support from dean and university administrators" and that factor was ranked in the top ten factors in the list of motivators (or demotivators) to participate in distance education. Since there was no clear interpretation of what would constitute greater support from the deans or administrators, it was hard to parse out what that would mean, but the studies cited generally indicated that "support" seemed to mean that the dean and administration understood the faculty member's commitment's to teaching online with accommodations, such as teaching load adjustment for teaching online, updated technology training and software updates, or simply by

who had previously taught online were quick to share their online teaching experiences with fellow faculty members, but there still seemed to be a general lack of awareness of the time involved in teaching online classes). In addition, little or no evidence may have been available to the faculty participating in these studies that would provide insights into time required to create the content and deliver course materials in online environments, or the time to work with the instructional designer or design teams that would co-create the online courses with the faculty serving as content matter expert for the online course(s). The steps involved in designing, implementing, and teaching online extended beyond traditional inclass preparation time, largely because online courses required more online interactivities, more graphic representations, and more attention to cultural implications, all while there were additional considerations, such as how the course materials would be housed within the learning management system, and how the technology would be utilized inside and outside the course (Ansah, & Johnson, 2003; Betts, 1998; Bender, Wood, & Vredevoogd, 2004; Cavanaugh, 2005; Chang, 2007; Chang 2008; French, 2001; Harley, 2002; Jacobsen, 2000;

A surprising finding was how low the ranking was over the factor of copyright ownership rights, and in some studies this factor was not even a consideration by faculty who had participated in the studies. Few faculty seemed to be concerned over copyright ownership rights, or royalties, at least as of the latest studies reviewed for this research (Betts, 2009; Gannon-Cook, Ley, Warner, & Crawford, 2010; Quinn & Trower, 2008; Wolcott, 2006; Zhen, Garthwait, & Pratt, 2008). (Concern over royalties was listed as one of the deterrents to faculty participating online in a number of the studies used in this research, but this factor ranked at most, fourth on the list of factors that could affect participation in online courses (Gannon-Cook, Ley, Crawford, Warner, 2009; Maguire, 2002, 2006; O'Quinn, & Corry, 2002; Schifter, 2000a, b; Wolcott, 1996, 2002a, b). Part of the reason for the lack of faculty interest or concern about ownership or royalties may have been because many faculty usually designed their own on-ground (traditional classroom) classes as a part of their teaching duties for the university; they did not generally copyright their courses, nor did they receive any royalties, or additional compensation for their courses. So it would follow that faculty that had not taught online would not think about copyrights or the potential problems that could surface with respect to contracts for designing courses that could either include payments for the design of courses and royalties, or "work for hire" courses designed for the university without any royalty rights. Without faculty being presented with the pros and cons of online course contracts, it would make sense that faculty who had not taught online previously would not pay attention to this factor when responding to surveys requesting

their prioritizing of copyright considerations in designing online courses.

Another factor that was mentioned in many studies (Beggs, 2002; Betts, 1998, 2008; Bollinger & Wasilik, 2009; Bower, 2002; O'Quinn, 2003; O'Quinn & Corry, 2002; Quinn & Trower, 2008; Wolcott, 2006; Zhen & Pratt, 2008) was "lack of support from dean and university administrators" and that factor was ranked in the top ten factors in the list of motivators (or demotivators) to participate in distance education. Since there was no clear interpretation of what would constitute greater support from the deans or administrators, it was hard to parse out what that would mean, but the studies cited generally indicated that "support" seemed to mean that the dean and administration understood the faculty member's commitment's to teaching online with accommodations, such as teaching load adjustment for teaching online, updated technology training and software updates, or simply by

Lazarus, 2003; Weller, 2006).

providing positive administrative support. In some comments generated from faculty it seemed that a number of respondents felt that faculty should be included in university decisions, particularly with respect to curricular and design decisions that affected both faculty and students. Other considerations, such as credit toward tenure, could also fall under the category of "support" and would also acknowledge the work involved in teaching online.

This study did not look at trends in other countries, so research will need to be conducted to see if the recommendations from studies, such as the ones cited herein, would be applicable to universities offering online courses and programs in those countries. For universities seeking to provide cross-border education, it would make sense to consider the cultures and traditions of those countries first and implement measures that honor those priorities, then conduct studies, similar to the ones cited here, that address the use of adjuncts and full-time faculty, and the bottom-line cost comparisons of using more adjuncts or full-time faculty to teach online courses.

One Taiwanese study of online students pointed to a lack of adequate mentoring or advice from experienced and knowledgeable faculty as one of the most frequently cited sources of delay in completing their degrees (Kuo, 2011). It was also mentioned in several Chinese studies (Chang, Martin, Schellens, 2010; Huang, Dedegikas, Walls,2009; Zhang, Zhang, Duan, Fu, Wang, 2010) that it was important for the university to be aligned with its mission and that "Chinese students are used to the classroom teaching style. The reason for this situation could be that the traditional face-to-face class is still the main style of teaching in China" (Khou, 2011, p.6). Questions were also raised about whether the universities studied articulated global learning as a goal for its undergraduates; there were also questions about how faculty were rewarded for their teaching and scholarship (Chapman, 2011) and there were comments that cross-border providers may not meet each country's priorities, and perhaps these locales are better served by their local universities than by foreign providers that more frequently deliver what they already have developed elsewhere.

A review of research on Brazilian universities (Abrahão & Malanga, 2010) revealed universities have been undergoing a turning point in Brazil's brief history of higher education (the first Brazilian university, the University of Sao Paulo, was only founded in 1934). Therefore, the analysis has evidenced that "public policies for higher education must signal if they want to provide professional teaching in higher education, geared toward the job market, or rather, teaching from a real university, thus basing its mission, ethos and episteme in the 21st century on knowledge production and transmission" (p.43). No doubt if the choice is the latter, then there will be a strong need for full-time tenure-track and tenured faculty. While Brazil is also turning more to online education, it appears that there will be an emphasis placed on a "real university" experience which may necessitate traditional university standards and faculty, and will include the enlistment of faculty to teach online, so motivating faculty will become more important as the universities continue to grow. Future studies may reveal the progress of online courses and degree programs throughout the major Brazilian universities over the next decade.

In summary, there were a number of factors cited in the thirty-eight studies that could enlist and retain faculty to teach online courses and also allow the universities to best focus on their student populations and financial needs. If some of these recommendations have been adopted in the universities studied, then perhaps there can be follow-up studies that can

Facts and Fiction:

2010).

teaching online?

*Actor-Network Analysis* 

Lessons from Research on Faculty Motivators and Incentives to Teach Online 69

Gannon-Cook, Ley, Crawford, Warner, 2009; Maguire, 2006; McLean, 2006b; O'Quinn, &

Maslow stated there were two basic levels of human needs that motivated humans: lower level needs, such as physiological, security; and, higher level needs, such as esteem of self and others, and self-actualization (Maslow, 1970). Once the lower level needs are met, those needs become less important and the motivation rises towards the higher level needs, yet the physiological and security needs must be met, or primal survival fears surface. Work has been modern human's way of earning the means of meeting the physiological and security needs, and without a feeling of some degree of security in that work, it is hard to move up to the next order of higher level needs. Extrinsic motivation emphasizes that satisfaction from an activity is contingent upon a reward; and extrinsic motivators include a variety of offerings for faculty, such as salary increases, merit pay, course overloads, tenure or university recognition in the form of lab space, and monetary stipends (Gannon-Cook,

If a large number of studies recommend that universities give faculty more extrinsic rewards and include faculty in major decisions, such as their distance learning, then why have so many universities remained intransigent about implementing these recommendations and offering these incentives to faculty? The answer is that, usually, college presidents, deans, and administrators of universities would respond with two words: "the economy." But avoiding, or worse, refusing, to consider faculty concerns or motivations, particularly about teaching online, could result in problems that could be avoided if university administrators addressed some of the important questions and concerns of faculty. So, what are the true savings and what are the potential costs of failing to consider the recommendations of the many studies that addressed faculty concerns about

In truth, some of the extrinsic factors with the highest rankings in the studies cited in this research (See Appendix A) would not cause the university to substantially increase its faculty budgets. These were: training, reduced teaching load, and support and encouragement from the dean or administration (Betts, 1998; Gannon-Cook, 2003; Johnston, Alexander, Conrad, & Fieser, 2000; Wolcott, 2002a, b). But highest on the list of factors that would positively influence faculty to teach online were: increase in salary, job security (credit towards tenure), and monetary support/stipends; these would all substantially add to the university's expenses. However, it bears reviewing the costs incurred for offering extrinsic rewards to the costs to enlist and retain faculty, particularly in online teaching. It might be worth exploring, particularly compared to the costs of not only the increased costs of offering any or all of these extrinsic motivators, but to also look at the costs of adjunct salaries, possible costs of lost opportunities for increased online course offerings, or, worse, the costs of student attrition or lower student enrollments due to unavailability of full-time faculty (Bender, Wood, & Vredevoogd, 2004; Benjamin, 2002; Florida, State of, 2011;

The actor-network theory is utilized to study social, economic, and cultural effects across academic disciplines, including higher education. (Callon, 1986; Latour, 1987, 2005; Law & Hassard, 1999; Law, 1992; Schibik, Harrington, 2004; Xenos, Pierrakaes, & Pintelas, 2002).

McArthur, 1999; McLean, 2006a; Xenos, Pierrakaes, & Pintelas, 2002).

Corry, 2004; Panda, & Mishra, 2008; Wolcott, 2006; Zhen, Garthwait, & Pratt, 2008).

generalize the findings so that they can be extended to other universities and to online crossborder education, or replicated in universities in other countries.

#### **3. Analysis**

This section describes the methods used to investigate variables that could motivate or inhibit faculty to teach and remain teaching online courses. This study utilized a qualitative, postpositivist methodology which conducted historical research to review over thirty-eight studies to uncover data that could reveal which factors best motivated faculty to participate in teaching and designing online courses. The analysis included thick description in order to uncover as much data as possible that could identify "reasonable implications for practice from their findings" (Gall, Borg, Gall, 1996, p.748).

In addition, the actor-network theory was also introduced as a viable methodology for this study since it is primarily used to study social, economic, and cultural effects, particularly in science and technology studies (STS), and to apply the STS principles across academic disciplines to higher education. (STS has already been documented effectively in disciplines other than science, most notably in organizational analysis, economics, sociology, and anthropology (Callon, 1986; Latour, 1987, 2005; Law & Hassard, 1999; Law, 1992). The relationships among technological innovations, faculty, and higher education administration are viewed in socially embedded contexts so as to get a more comprehensive look at how the findings and recommendations from the studies on faculty motivation could be utilized effectively.

#### *Historical Analysis*

In an effort to better understand how faculty, as an important element in student retention and successful completion of online courses, can be motivated to teach and remain teaching online courses, extensive research into studies on faculty motivation was undertaken by the researcher; at least 36 studies on faculty motivation were found which utilized a variety of quantitative and mixed-method methodologies. In addition a thorough literature review (see Review of Literature) supplemented the data provided from the studies. Repeatedly, intrinsic motivation was cited as an important contributor to faculty motivation to teach online, primarily because teaching online allows students to take courses and pursue a degree when they may not, otherwise, have access or opportunity to do so; and the desire to be helpful, along with the chance to contribute to the betterment of mankind, assures the inclination of faculty to take up the challenge of teaching online. Studies prior to 1999 (Bonk, 2001; Bower, 2002; Johnston, Alexander, Conrad, & Fieser, 2000; Maguire, 2002; O'Quinn, & Corry, 2002; Schifter, 2000a, b; Wolcott, 1996) included a larger number of early DE adopters who were more intrinsically motivated to participate in DE. (An early adopter is someone who takes on or embraces an innovation in the early phases of implementation). These respondents reported that the intrinsic rewards of accomplishment were enough incentive to participate in DE (Bonk, 2000; French, 2001; Husman & Miller, 1999; Johnston, 1999; Wilson, 1999). However, surveys conducted after 2000 included responses from more late adopters of DE (a late adopter being someone who has reservations about implementing the innovation and refrains from adoption until he/she is more comfortable with the innovation), and these respondents were more motivated by extrinsic rewards (Beggs, 2002; Bower, 2002; Johnston, 2000; Bollinger & Wasilik, 2009; Bruner, 2007; Cavanaugh, 2005;

generalize the findings so that they can be extended to other universities and to online cross-

This section describes the methods used to investigate variables that could motivate or inhibit faculty to teach and remain teaching online courses. This study utilized a qualitative, postpositivist methodology which conducted historical research to review over thirty-eight studies to uncover data that could reveal which factors best motivated faculty to participate in teaching and designing online courses. The analysis included thick description in order to uncover as much data as possible that could identify "reasonable implications for practice

In addition, the actor-network theory was also introduced as a viable methodology for this study since it is primarily used to study social, economic, and cultural effects, particularly in science and technology studies (STS), and to apply the STS principles across academic disciplines to higher education. (STS has already been documented effectively in disciplines other than science, most notably in organizational analysis, economics, sociology, and anthropology (Callon, 1986; Latour, 1987, 2005; Law & Hassard, 1999; Law, 1992). The relationships among technological innovations, faculty, and higher education administration are viewed in socially embedded contexts so as to get a more comprehensive look at how the findings and recommendations from the studies on faculty motivation could

In an effort to better understand how faculty, as an important element in student retention and successful completion of online courses, can be motivated to teach and remain teaching online courses, extensive research into studies on faculty motivation was undertaken by the researcher; at least 36 studies on faculty motivation were found which utilized a variety of quantitative and mixed-method methodologies. In addition a thorough literature review (see Review of Literature) supplemented the data provided from the studies. Repeatedly, intrinsic motivation was cited as an important contributor to faculty motivation to teach online, primarily because teaching online allows students to take courses and pursue a degree when they may not, otherwise, have access or opportunity to do so; and the desire to be helpful, along with the chance to contribute to the betterment of mankind, assures the inclination of faculty to take up the challenge of teaching online. Studies prior to 1999 (Bonk, 2001; Bower, 2002; Johnston, Alexander, Conrad, & Fieser, 2000; Maguire, 2002; O'Quinn, & Corry, 2002; Schifter, 2000a, b; Wolcott, 1996) included a larger number of early DE adopters who were more intrinsically motivated to participate in DE. (An early adopter is someone who takes on or embraces an innovation in the early phases of implementation). These respondents reported that the intrinsic rewards of accomplishment were enough incentive to participate in DE (Bonk, 2000; French, 2001; Husman & Miller, 1999; Johnston, 1999; Wilson, 1999). However, surveys conducted after 2000 included responses from more late adopters of DE (a late adopter being someone who has reservations about implementing the innovation and refrains from adoption until he/she is more comfortable with the innovation), and these respondents were more motivated by extrinsic rewards (Beggs, 2002; Bower, 2002; Johnston, 2000; Bollinger & Wasilik, 2009; Bruner, 2007; Cavanaugh, 2005;

border education, or replicated in universities in other countries.

from their findings" (Gall, Borg, Gall, 1996, p.748).

**3. Analysis** 

be utilized effectively.

*Historical Analysis* 

Gannon-Cook, Ley, Crawford, Warner, 2009; Maguire, 2006; McLean, 2006b; O'Quinn, & Corry, 2004; Panda, & Mishra, 2008; Wolcott, 2006; Zhen, Garthwait, & Pratt, 2008).

Maslow stated there were two basic levels of human needs that motivated humans: lower level needs, such as physiological, security; and, higher level needs, such as esteem of self and others, and self-actualization (Maslow, 1970). Once the lower level needs are met, those needs become less important and the motivation rises towards the higher level needs, yet the physiological and security needs must be met, or primal survival fears surface. Work has been modern human's way of earning the means of meeting the physiological and security needs, and without a feeling of some degree of security in that work, it is hard to move up to the next order of higher level needs. Extrinsic motivation emphasizes that satisfaction from an activity is contingent upon a reward; and extrinsic motivators include a variety of offerings for faculty, such as salary increases, merit pay, course overloads, tenure or university recognition in the form of lab space, and monetary stipends (Gannon-Cook, 2010).

If a large number of studies recommend that universities give faculty more extrinsic rewards and include faculty in major decisions, such as their distance learning, then why have so many universities remained intransigent about implementing these recommendations and offering these incentives to faculty? The answer is that, usually, college presidents, deans, and administrators of universities would respond with two words: "the economy." But avoiding, or worse, refusing, to consider faculty concerns or motivations, particularly about teaching online, could result in problems that could be avoided if university administrators addressed some of the important questions and concerns of faculty. So, what are the true savings and what are the potential costs of failing to consider the recommendations of the many studies that addressed faculty concerns about teaching online?

In truth, some of the extrinsic factors with the highest rankings in the studies cited in this research (See Appendix A) would not cause the university to substantially increase its faculty budgets. These were: training, reduced teaching load, and support and encouragement from the dean or administration (Betts, 1998; Gannon-Cook, 2003; Johnston, Alexander, Conrad, & Fieser, 2000; Wolcott, 2002a, b). But highest on the list of factors that would positively influence faculty to teach online were: increase in salary, job security (credit towards tenure), and monetary support/stipends; these would all substantially add to the university's expenses. However, it bears reviewing the costs incurred for offering extrinsic rewards to the costs to enlist and retain faculty, particularly in online teaching. It might be worth exploring, particularly compared to the costs of not only the increased costs of offering any or all of these extrinsic motivators, but to also look at the costs of adjunct salaries, possible costs of lost opportunities for increased online course offerings, or, worse, the costs of student attrition or lower student enrollments due to unavailability of full-time faculty (Bender, Wood, & Vredevoogd, 2004; Benjamin, 2002; Florida, State of, 2011; McArthur, 1999; McLean, 2006a; Xenos, Pierrakaes, & Pintelas, 2002).

#### *Actor-Network Analysis*

The actor-network theory is utilized to study social, economic, and cultural effects across academic disciplines, including higher education. (Callon, 1986; Latour, 1987, 2005; Law & Hassard, 1999; Law, 1992; Schibik, Harrington, 2004; Xenos, Pierrakaes, & Pintelas, 2002).

Facts and Fiction:

*Cognitive load.* 

*Other factors.* 

affect student cognitive loads in online courses.

Lessons from Research on Faculty Motivators and Incentives to Teach Online 71

were problems experienced by students until long after the course when they receive their course evaluations, particularly if students don't put them on notice about their questions or concerns during the course. (Depending on the online environment and personalities of the students, they may not feel comfortable enough to voice concerns while in the class unless the instructor has made concerted efforts to encourage an open collaborative environment.) The students may also be experiencing cognitive load issues associated with added stress from being in an online environment, from having to go outside the course to wikis or Wimbas, or VOIP (voice over internet protocol) sites where they must participate and post

Cognitive load issues can be important considerations in online courses (Gannon Cook & Crawford, 2009). The learner can easily become overwhelmed with information and requirements, therefore, the online course should be structured simply to present information that progressively develops a cognitive and conceptual framework of understanding on the part of the learner. The learner must develop a knowledge base before moving on to the next bit of knowledge; a new learner may take a longer period of time to understand and develop an understanding of the subject than a learner with prior knowledge and understanding of the subject matter. "Then, once expertise is gained the newly crowned expert can reinvest the extra cognitive load into other things" (Wilson, 21 July 2008, ¶ 3). To-date, while the topic of cognitive load has been extensively discussed, there are few studies that have provided sufficient data so as to show there is a significant cognitive load impact on students participating in online courses, likely due to the fact that affective factors vary by student, such as the diverse student knowledge and skill levels (Paas, Renkl, & Sweller, 2003), as well as a number of other factors that could affect or not

In addition to cognitive load, other factors, such as the factors of cultural backgrounds, socioeconomic status, technological proficiency, and learning styles on the part of the students; and, content materials, online course design, learning management systems, as well as philosophical beliefs on the part of the teachers, there are yet other factors that are seldom even mentioned in the studies reviewed in this research. For example, language considerations could affect learning abilities of first-generation students; same for cultural

Because there are so many unexplored factors that could contribute to the big picture of faculty motivation, the actor-network approach was utilized in this study to see if there were any consistent factors that could point to interactions with faculty teaching online in the courses researched, with design features, or with other actor-network factors that could shed light on faculty teaching online. The hope was that inferences could be made from these studies on which factors best motivate faculty participation in online courses. But without conducting more studies that parsed out individual factors that could have significant impact upon both online students and the faculty teaching or considering teaching those courses, it would be difficult to assign any attribution to actor-network factors in the assessment of what motivated faculty to teach online. Further research could

courtesies that may keep some students from participating more fully.

their feedback or assignments (Paas, Renkl, & Sweller, 2003; Sweller, 1994, 1999).

Actor-network theory (ANT) is a type of methodology which looks at the agency of nonhuman issues and the effects of technology and other research factors upon humans and it has been used primarily in the fields of science, but has expanded in its applications across many other academic disciplines.

The basic premise of actor-network analysis is that no environment exists in a vacuum; in higher education, factors that affect faculty also affect higher educational administration, and both affect and are affected by technologies. Socially embedded contexts provide insights into the big picture—and how all of these factors converge to influence the entire environment and how the outcomes of decisions made with respect to each factor, are in turn, affected. In this research, the studies on faculty motivation that were reviewed provided invaluable insights into how their findings and recommendations could be utilized effectively.

Actor-network theory looks at how environments and networks act as a whole. An example, in the higher education elearning environment, students in an online course are encouraged to share their sociocultural backgrounds in their introductions, thus incorporating their cultural histories into their student experiences. They post their introductions via the learning management system; they share their experiences from their lives with the instructor and students in the class; and they integrate their experiences into the context of the online discussion using the technology and learning management system (LMS), yet another interface. All of these different environments and elements are brought together into a network to form a coherent whole.

According to actor-network theory, such actor-networks are potentially transient, existing in a constant making and re-making. This means that relations need to be repeatedly "performed" … (The teachers need to come to work each day, and the computers need to keep on running.)…Networks of relations are not intrinsically coherent, and may indeed contain conflicts (there may be adversarial relations between teachers/children, or computer software may be incompatible). Social relations, in other words, are only ever in process, and must be performed continuously (Wikipedia, 2011 http://en.wikipedia.org/wiki/ Actor-network\_theory)

In the research conducted in this study, factors addressed intrinsic and extrinsic factors that motivated or de-motivated faculty to participate in online courses; few addressed nonhuman issues. None addressed nonhuman issues in any detail, such as the effects of technology, learning management systems, and other research factors, such as online course development and design factors. Yet, no environment exists in a vacuum; factors that students affect faculty; factors that affect faculty affect higher education administration; both affect and are affected by technologies; and, all of these factors affect students and student retention. Socially embedded contexts provide insights into the big picture; by introducing a search for actor-network factors the researcher was able to take a look at how the dynamics of diverse student learners, their interaction with the technology and LMS, with each other, and with the instructor, and all of the observed participants' interactions in each module's discussions of the course's subject matter, provided a big picture perspective of the course and participants. Too often the instructor is so busy juggling the course materials, the technologies, the internal and external email student correspondences, the discussion posts, and their other faculty responsibilities, that they have little time to take a minute while in situ to look at patterns that are occurring in the course. They may not even realize there were problems experienced by students until long after the course when they receive their course evaluations, particularly if students don't put them on notice about their questions or concerns during the course. (Depending on the online environment and personalities of the students, they may not feel comfortable enough to voice concerns while in the class unless the instructor has made concerted efforts to encourage an open collaborative environment.)

The students may also be experiencing cognitive load issues associated with added stress from being in an online environment, from having to go outside the course to wikis or Wimbas, or VOIP (voice over internet protocol) sites where they must participate and post their feedback or assignments (Paas, Renkl, & Sweller, 2003; Sweller, 1994, 1999).

#### *Cognitive load.*

70 E-Learning – Engineering, On-Job Training and Interactive Teaching

Actor-network theory (ANT) is a type of methodology which looks at the agency of nonhuman issues and the effects of technology and other research factors upon humans and it has been used primarily in the fields of science, but has expanded in its applications across

The basic premise of actor-network analysis is that no environment exists in a vacuum; in higher education, factors that affect faculty also affect higher educational administration, and both affect and are affected by technologies. Socially embedded contexts provide insights into the big picture—and how all of these factors converge to influence the entire environment and how the outcomes of decisions made with respect to each factor, are in turn, affected. In this research, the studies on faculty motivation that were reviewed provided invaluable insights into how their findings and recommendations could be

Actor-network theory looks at how environments and networks act as a whole. An example, in the higher education elearning environment, students in an online course are encouraged to share their sociocultural backgrounds in their introductions, thus incorporating their cultural histories into their student experiences. They post their introductions via the learning management system; they share their experiences from their lives with the instructor and students in the class; and they integrate their experiences into the context of the online discussion using the technology and learning management system (LMS), yet another interface. All of these different environments and elements are brought together

According to actor-network theory, such actor-networks are potentially transient, existing in a constant making and re-making. This means that relations need to be repeatedly "performed" … (The teachers need to come to work each day, and the computers need to keep on running.)…Networks of relations are not intrinsically coherent, and may indeed contain conflicts (there may be adversarial relations between teachers/children, or computer software may be incompatible). Social relations, in other words, are only ever in process, and must be performed continuously (Wikipedia, 2011 http://en.wikipedia.org/wiki/

In the research conducted in this study, factors addressed intrinsic and extrinsic factors that motivated or de-motivated faculty to participate in online courses; few addressed nonhuman issues. None addressed nonhuman issues in any detail, such as the effects of technology, learning management systems, and other research factors, such as online course development and design factors. Yet, no environment exists in a vacuum; factors that students affect faculty; factors that affect faculty affect higher education administration; both affect and are affected by technologies; and, all of these factors affect students and student retention. Socially embedded contexts provide insights into the big picture; by introducing a search for actor-network factors the researcher was able to take a look at how the dynamics of diverse student learners, their interaction with the technology and LMS, with each other, and with the instructor, and all of the observed participants' interactions in each module's discussions of the course's subject matter, provided a big picture perspective of the course and participants. Too often the instructor is so busy juggling the course materials, the technologies, the internal and external email student correspondences, the discussion posts, and their other faculty responsibilities, that they have little time to take a minute while in situ to look at patterns that are occurring in the course. They may not even realize there

many other academic disciplines.

into a network to form a coherent whole.

utilized effectively.

Actor-network\_theory)

Cognitive load issues can be important considerations in online courses (Gannon Cook & Crawford, 2009). The learner can easily become overwhelmed with information and requirements, therefore, the online course should be structured simply to present information that progressively develops a cognitive and conceptual framework of understanding on the part of the learner. The learner must develop a knowledge base before moving on to the next bit of knowledge; a new learner may take a longer period of time to understand and develop an understanding of the subject than a learner with prior knowledge and understanding of the subject matter. "Then, once expertise is gained the newly crowned expert can reinvest the extra cognitive load into other things" (Wilson, 21 July 2008, ¶ 3). To-date, while the topic of cognitive load has been extensively discussed, there are few studies that have provided sufficient data so as to show there is a significant cognitive load impact on students participating in online courses, likely due to the fact that affective factors vary by student, such as the diverse student knowledge and skill levels (Paas, Renkl, & Sweller, 2003), as well as a number of other factors that could affect or not affect student cognitive loads in online courses.

#### *Other factors.*

In addition to cognitive load, other factors, such as the factors of cultural backgrounds, socioeconomic status, technological proficiency, and learning styles on the part of the students; and, content materials, online course design, learning management systems, as well as philosophical beliefs on the part of the teachers, there are yet other factors that are seldom even mentioned in the studies reviewed in this research. For example, language considerations could affect learning abilities of first-generation students; same for cultural courtesies that may keep some students from participating more fully.

Because there are so many unexplored factors that could contribute to the big picture of faculty motivation, the actor-network approach was utilized in this study to see if there were any consistent factors that could point to interactions with faculty teaching online in the courses researched, with design features, or with other actor-network factors that could shed light on faculty teaching online. The hope was that inferences could be made from these studies on which factors best motivate faculty participation in online courses. But without conducting more studies that parsed out individual factors that could have significant impact upon both online students and the faculty teaching or considering teaching those courses, it would be difficult to assign any attribution to actor-network factors in the assessment of what motivated faculty to teach online. Further research could

Facts and Fiction:

teaching online).

in this research.

**4. Summary** 

seek further research in this arena.

efforts' in teaching online courses.

Lessons from Research on Faculty Motivators and Incentives to Teach Online 73

Ley, Crawford, Warner, 2009; Johnston, Alexander, Conrad, & Fieser, 2000; Wolcott, 2002). Factors that had next motivated faculty had been: technical and administrative support (which included not only technical support, but the ability to have their faculty voices heard), and job advancement (administration recognition of faculty efforts invested in

Interestingly, while tenure and promotion were discussed in a number of the studies, it did not rank high in the rankings of motivators to faculty in any of the studies cited in this research, perhaps because many faculty did not see it as a factor that should be included in consideration of teaching online. It may also be that faculty felt that many universities have been moving away from tenure as a part of the university structure, but the reasons for the low ranking of tenure consideration in teaching online was not clear from the studies cited

While the thirty-eight studies researched herein did not address how non-human factors could have effects on faculty motivation to teach online, the need for future research in this area was put forth in this study. A look at the big picture and the effects of these non-human factors on faculty motivation to teach online could provide important insights into not only faculty motivation, but also on student retention. Research, such as the study of actornetwork approaches could yield thick data that could benefit higher education administrative bottom line decisions and yield positive long-range plans for universities that

The facts, thus far, shed light on the fiction that faculty will teach online happily just because it is their nature to be facilitative as teachers. Lessons learned from the thirty-eight studies in this research on faculty motivators to teach online provide strong indicators of which factors

In the end, the fact remains that online courses will continue to grow and students will increasingly be attracted to the convenience of online learning. Universities are competing with other universities around the world, so the challenges to enlist and retain online faculty that were addressed in this study reflect what universities all over the world either are or will be experiencing in the near future. Whether there are a small or large number of faculty members participating in online teaching may depend largely on whether faculty members

There does not seem to be a "one size fits all" solution as regards faculty motivational factors as regards participation in DE. The trend for universities to continue to expand DE courses, due to increased consumer demand and cost effectiveness will continue. "Higher education is no longer a sanctuary against a global marketplace for educational products" (Gannon-Cook, 2010, p.135); and institutions of higher education must meet the demands of its clients. As universities move past the introductory phases of elearning, and into a culture of integrated online course delivery, research that takes into consideration the entire picture of the university environment should be considered. Administrators must not only meet the demands for DE, but also the needs of its faculty, higher education's most important assets. Higher education must address faculty needs, so as to more appropriately support faculty

are the most successful in enlisting and retaining faculty to teach online.

feel valued in their online efforts and that their voices are heard.

shed more light on best elearning practices to contribute to the lessons learned on faculty motivators and incentives to teach online.

The research in these thirty-eight studies that could identify "reasonable implications for practice from their findings" (Gall, Borg, Gall, 1996, p.748) for faculty considering or teaching online seemed to revolve around the preponderance of courses that cited first intrinsic motivation as the top motivator, particularly referencing faculty members' desires as teachers, to be helpful to their students, and help students find ways to take advantage of online courses to advance their education. The availability of online courses and programs and convenience were intrinsically encouraging to teachers who want to see students who may, otherwise, not be able to go to college have a way to earn their degrees. These reasons seemed to be de facto motivators for faculty who had entered the teaching profession to be of service to others, the studies, never the less named them as the primary motivators.

But many faculty already carry full or overload teaching and administrative workloads, so pride of helping students achieve and personal accomplishment and might not sustain continued DE instruction without the reinforcement of some type of external motivation. There needed to be some other factors that could better assure faculty participation and retention in teaching online courses.

Universities often take the stance that DE will be integrated into traditional curricula, requiring faculty to teach DE and e-courses as a part of their teaching load. However, the researcher wanted to look at this stance, particularly with respect to second and third generation DE faculty. Since the preponderance of the studies indicated that, after early adopters had taught online they often demurred from teaching again (Beggs, 2002; Bower, 2002; Betts, 2009; Betts & Sikorski, 2008; Birch, Burnett, 2009; Bruner, 2007; Gannon-Cook, Ley, Crawford, Warner, 2009; Johnston, Alexander, Conrad, & Fieser, 2000; O'Quinn & Corry, 2002, 2004; Panda & Mishra, 2008, Parker, 2003; Quinn, Trower, 2008), it seemed that finding which factors that could not only incentivize faculty to participate in teaching online, but to continue teaching online, could prove informative.

Some collaborative approaches between administration and faculty could create ongoing dialog for enlisting and retaining faculty teaching online courses and set the tone for future stability of faculty teaching online courses. The "carrot and stick" motivation was explored in several studies (Betts, 1998; Schifter, 2002; Wolcott, 1996), with the intention of exploring which key incentives were successful as rewards for teaching online. The results in the majority of the studies had indicated extrinsic motivators were ranking the highest, even as the intrinsic motivators had shown to be powerful, but the keys to getting and keeping faculty engaged and enlisted in teaching online seemed to rest with extrinsic motivators (Beggs, 2002; Bower, 2002; Betts, 2009; Betts & Sikorski, 2008; Birch, Burnett, 2009; Bruner, 2007; Gannon-Cook, Ley, Crawford, Warner, 2009; Johnston, Alexander, Conrad, & Fieser, 2000; O'Quinn & Corry, 2002, 2004; Panda & Mishra, 2008, Parker, 2003; Quinn, Trower, 2008; Wolcott, 2002). Extrinsic rewards consisting largely of monetary rewards, primarily salary, course releases, and course stipends; it also included the reverse, demotivators, such as insufficient rewards (inadequate or no salary increases, course releases, or stipends); these seemed to be the key motivators (or demotivators) to faculty to participate in teaching online. As much as one-fourth (25%) of these combined motivators and demotivators in the studies suggested these factors weighted greater than their individual factor loadings, thus raising the likelihood these motivators could provide successful motivation (Gannon-Cook, Ley, Crawford, Warner, 2009; Johnston, Alexander, Conrad, & Fieser, 2000; Wolcott, 2002). Factors that had next motivated faculty had been: technical and administrative support (which included not only technical support, but the ability to have their faculty voices heard), and job advancement (administration recognition of faculty efforts invested in teaching online).

Interestingly, while tenure and promotion were discussed in a number of the studies, it did not rank high in the rankings of motivators to faculty in any of the studies cited in this research, perhaps because many faculty did not see it as a factor that should be included in consideration of teaching online. It may also be that faculty felt that many universities have been moving away from tenure as a part of the university structure, but the reasons for the low ranking of tenure consideration in teaching online was not clear from the studies cited in this research.

#### **4. Summary**

72 E-Learning – Engineering, On-Job Training and Interactive Teaching

shed more light on best elearning practices to contribute to the lessons learned on faculty

The research in these thirty-eight studies that could identify "reasonable implications for practice from their findings" (Gall, Borg, Gall, 1996, p.748) for faculty considering or teaching online seemed to revolve around the preponderance of courses that cited first intrinsic motivation as the top motivator, particularly referencing faculty members' desires as teachers, to be helpful to their students, and help students find ways to take advantage of online courses to advance their education. The availability of online courses and programs and convenience were intrinsically encouraging to teachers who want to see students who may, otherwise, not be able to go to college have a way to earn their degrees. These reasons seemed to be de facto motivators for faculty who had entered the teaching profession to be of service to others, the studies, never the less named them as the primary motivators.

But many faculty already carry full or overload teaching and administrative workloads, so pride of helping students achieve and personal accomplishment and might not sustain continued DE instruction without the reinforcement of some type of external motivation. There needed to be some other factors that could better assure faculty participation and

Universities often take the stance that DE will be integrated into traditional curricula, requiring faculty to teach DE and e-courses as a part of their teaching load. However, the researcher wanted to look at this stance, particularly with respect to second and third generation DE faculty. Since the preponderance of the studies indicated that, after early adopters had taught online they often demurred from teaching again (Beggs, 2002; Bower, 2002; Betts, 2009; Betts & Sikorski, 2008; Birch, Burnett, 2009; Bruner, 2007; Gannon-Cook, Ley, Crawford, Warner, 2009; Johnston, Alexander, Conrad, & Fieser, 2000; O'Quinn & Corry, 2002, 2004; Panda & Mishra, 2008, Parker, 2003; Quinn, Trower, 2008), it seemed that finding which factors that could not only incentivize faculty to participate in teaching

Some collaborative approaches between administration and faculty could create ongoing dialog for enlisting and retaining faculty teaching online courses and set the tone for future stability of faculty teaching online courses. The "carrot and stick" motivation was explored in several studies (Betts, 1998; Schifter, 2002; Wolcott, 1996), with the intention of exploring which key incentives were successful as rewards for teaching online. The results in the majority of the studies had indicated extrinsic motivators were ranking the highest, even as the intrinsic motivators had shown to be powerful, but the keys to getting and keeping faculty engaged and enlisted in teaching online seemed to rest with extrinsic motivators (Beggs, 2002; Bower, 2002; Betts, 2009; Betts & Sikorski, 2008; Birch, Burnett, 2009; Bruner, 2007; Gannon-Cook, Ley, Crawford, Warner, 2009; Johnston, Alexander, Conrad, & Fieser, 2000; O'Quinn & Corry, 2002, 2004; Panda & Mishra, 2008, Parker, 2003; Quinn, Trower, 2008; Wolcott, 2002). Extrinsic rewards consisting largely of monetary rewards, primarily salary, course releases, and course stipends; it also included the reverse, demotivators, such as insufficient rewards (inadequate or no salary increases, course releases, or stipends); these seemed to be the key motivators (or demotivators) to faculty to participate in teaching online. As much as one-fourth (25%) of these combined motivators and demotivators in the studies suggested these factors weighted greater than their individual factor loadings, thus raising the likelihood these motivators could provide successful motivation (Gannon-Cook,

online, but to continue teaching online, could prove informative.

motivators and incentives to teach online.

retention in teaching online courses.

While the thirty-eight studies researched herein did not address how non-human factors could have effects on faculty motivation to teach online, the need for future research in this area was put forth in this study. A look at the big picture and the effects of these non-human factors on faculty motivation to teach online could provide important insights into not only faculty motivation, but also on student retention. Research, such as the study of actornetwork approaches could yield thick data that could benefit higher education administrative bottom line decisions and yield positive long-range plans for universities that seek further research in this arena.

The facts, thus far, shed light on the fiction that faculty will teach online happily just because it is their nature to be facilitative as teachers. Lessons learned from the thirty-eight studies in this research on faculty motivators to teach online provide strong indicators of which factors are the most successful in enlisting and retaining faculty to teach online.

In the end, the fact remains that online courses will continue to grow and students will increasingly be attracted to the convenience of online learning. Universities are competing with other universities around the world, so the challenges to enlist and retain online faculty that were addressed in this study reflect what universities all over the world either are or will be experiencing in the near future. Whether there are a small or large number of faculty members participating in online teaching may depend largely on whether faculty members feel valued in their online efforts and that their voices are heard.

There does not seem to be a "one size fits all" solution as regards faculty motivational factors as regards participation in DE. The trend for universities to continue to expand DE courses, due to increased consumer demand and cost effectiveness will continue. "Higher education is no longer a sanctuary against a global marketplace for educational products" (Gannon-Cook, 2010, p.135); and institutions of higher education must meet the demands of its clients. As universities move past the introductory phases of elearning, and into a culture of integrated online course delivery, research that takes into consideration the entire picture of the university environment should be considered. Administrators must not only meet the demands for DE, but also the needs of its faculty, higher education's most important assets. Higher education must address faculty needs, so as to more appropriately support faculty efforts' in teaching online courses.

Facts and Fiction:

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Paying some attention to at least a few of the extrinsic motivators mentioned in this study may offer solutions that prove cost-effective to meet the burgeoning needs of online learning. The price to universities who take the time to address faculty motivators may be well worth the investment.

#### **5. Appendix A**

#### **List of Thirty-Eight Studies Researched in This Study (Also See Study References)**

*Facts and Fiction: Lessons From Research on Faculty Motivators and Incentives to Teach Online:* 

Akbulut, Y., Kuzu, A., Latchem, C., Odabasi, F. (2007) Allen, I. E. and Seaman, J. (2008) Beggs, T. A. (2002 Bender, D., Wood, B., & Vredevoogd, J. (2004). Betts, K. (1998). Betts, K. S. & Sikorski, B. (2008) Betts, K. S. (2009) Birch, D., Burnett, B. (2009) Bollinger, D. U., Wasilik, O. (2009). Bower, B. (2002) Bruner, J. (2007) Cavanaugh, (2005) Chang, C. L. (2008) French, R. C. (2001) Gannon-Cook, R., Ley, K., Crawford, C., Warner, A. (2009). Halawi, L. and McCarthy, R. (2007). Husmann, D., & Miller, M. (2001) Jacobsen, D., M. (2000) Johnston, T.C., Alexander, L, Conrad, C, & Fieser, J. (2000). Johnstone, S. M. (2001, February). Jones, S. and Johnson-Yale, C. (2005). Kwoumka, S. & Gannon-Cook, R. (2003) Kosak, L., Manning, D., Dobson, E., Rogerson, L., Cotnam, S., Colaric, S., & McFadden, C. (2004). Maguire, L.L. (2002). McLean, J. (2006) Neuhauser, C. (2002) Ngu, B. H. (2002). O'Quinn, L., & Corry, M. (2002) O'Quinn, L. R., & Corry, M. (2004) Panda, S. & Mishra, S. (2008). Parker, A. (2003). Quinn, K., Trower, C. (2008). Schifter, C. (2000a), Soldner, L.B., Lee, Y.R. Duby, P.B. (2004) West, R. E., Waddoups, G., & and Graham, C. R. (2007) Wolcott, L. L. (1996)

Xenos, M. Pierrakaes, C., & Pintelas, P. (2002) Zhen, A. Garthwait, A., & Pratt, P. (2008)

#### **6. References**

74 E-Learning – Engineering, On-Job Training and Interactive Teaching

Paying some attention to at least a few of the extrinsic motivators mentioned in this study may offer solutions that prove cost-effective to meet the burgeoning needs of online learning. The price to universities who take the time to address faculty motivators may be

**List of Thirty-Eight Studies Researched in This Study (Also See Study References)**  *Facts and Fiction: Lessons From Research on Faculty Motivators and Incentives to Teach Online:* 

Kosak, L., Manning, D., Dobson, E., Rogerson, L., Cotnam, S., Colaric, S., & McFadden, C.

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**Part 2** 

**E-Learning for Engineering,** 

**Medical Education and Biological Education** 

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