**3. KEPack**

platform of e-Learning system in higher educational institution. This study mainly analyzes learners' experience on the e-Learning and translation of emotion to e-Learning interface.

E-Learning is a learning model that involves information and communication technology into learning activities. Better learning process will be gained without changing conventional learning model. E-Learning plays important role in learning, because it provides learning tools to support learning process optimally, but it is not to replace totally the conventional learning. Nowadays, according to the growth of information technology, many education institutions such as high education try to implement e-Learning to provide better learning environment based on information technology, to give better service of learning activities. E-Learning will support conventional learning by applying information technology, in order to improve learners' learning performance and learning experiences to get more valuable knowledge.

Another name of e-Learning is virtual learning, and in general it can be divided into two

• Synchronous system. All learners are required to participate on learning process in the same time and are provided by tool to collaborate with other learners simultaneously. It

• Asynchronous system. In this case all learners are given flexible time to access the system due to each phase or condition of learning. Learners are provided to be able to communi-

Most of e-Learning systems either open source or proprietary are generally web-based system and have similar functions or have standard functions to support learning process effectively and efficiently through the Internet. It is possible for education institution to analyze and select the best one to be implemented in their institution. In e-Learning system, the important thing is not only learning functions; learning environment is also an important thing such as interface which acts as link between learners and system. Interface plays a key role in making learners' motivation of using e-Learning for a long time with pleasure and without

The interface's body of web-based e-Learning system commonly is similar to common webbased information system. Basically, it should consist of many design elements as follows:

will impact on improvement of knowledge achievement during learning.

cate with each other using collaboration tools such as bulletin board system.

**2. E-Learning system**

108 Trends in E-learning

types as follows:

depression.

• Header and footer

• Background (logo and color) • Font (type, size, and color)

• Menu (position, type, and style)

This study adopts method of Kansei Engineering Type I (KEPack) due to its simplicity and wide use in many product developments [2, 6, 10, 19, 20, 22, 23]. **Figure 3** shows the systematic processes of KEPack. This study uses KEPack as methodology to process the inputted data from learners about what they emotionally feel after exploring each the interface of e-Learning system.

At least five open source e-Learning systems [24–28] can be selected as specimens based on its suitability to be adopted in the academic institution's environment, to be used as specimen in the Kansei evaluation session. The specimens are selected based on their visible differences in design characteristics such as background color and page layout. The selected five specimens in this study are Moodle, Efront, Opigno, Chamilo, and ATutor. Ten Kansei words representing psychological feeling are selected to represent psychological responses learners have with the specimens. The Kansei words are as shown in **Table 1**. This study constructs each Kansei words to five-point semantic differential (SD) scale to be used as measurement instrument in the Kansei evaluation session. In this study one hundred learners are involved as participants. Participants consist of first-grade university students. For further analysis data collection can be categorized according to gender, age, and so on. Learners are required to give responses

**Figure 3.** KEPack methodology [11].


**Table 1.** Questionnaire using Kansei words and five-point scale semantic differential.

toward the specimens of e-Learning system. Each specimen is shown for a limited time one by one in an experimental room; all participants rate their Kansei responses to fill the score (from 1 to 5) of every Kansei word into the Kansei checklist.

Multivariate analysis is performed to analyze the average data obtained from the evaluation session. Existing software statistic such as XLStat or SPSS can be used for calculating questionnaire data. Finally, this study provides a recommendation of the desired open source e-Learning system to be used in supporting learning process.

#### **3.1. Data collection**

In this study, questionnaire data from all participants are collected and then calculated its average as shown in **Table 2**.

The data is analyzed by two kinds of multivariate analysis: principal component analysis for analyzing distribution of Kansei words and specimens and factor analysis for exploring the biggest emotion.

#### **3.2. Coefficient correlation analysis**

The relationship between emotions represented by Kansei words is shown in **Table 3**. According to this result, Kansei word relationship can be divided into six categories such as very strong, strong, enough, weak, very weak, and no relationship. For example, the emotion of bright has strong relationship with the emotion of dynamic, but has no relationship with simple.

of the variability for each D1 to D4. The level of variability of D1 is 47.771%; D2 is 29.407%, respectively. The total cumulative value of D1 and D2 is 77.178% more than threshold value of 70%. It means that these two factors of D1 and D2 can be used for further analysis because these factors have enough influence for representing learners' emotions toward the five open

**Dynamic Informative Bright Harmony Comfort Rigid Simple Unique Passion Formal**

Dynamic 1 −0.510 0.967 0.819 0.778 −0.456 −0.802 −0.493 −0.068 −0.539 Informative 1 −0.557 −0.790 −0.158 −0.057 0.348 −0.494 −0.549 0.714 Bright 1 0.885 0.609 −0.464 −0.626 −0.429 −0.011 −0.639 Harmony 1 0.320 −0.502 −0.458 −0.046 0.037 −0.909 Comfort 1 −0.141 −0.958 −0.592 −0.062 0.038 Rigid 1 0.210 0.530 0.840 0.606 Simple 1 0.419 0.056 0.156 Unique 1 0.623 −0.154 Passion 1 0.111 Formal 1

**1 2 3 4 5**

Analysis Learners' Preference in E-Learning System Using Kansei Approach

http://dx.doi.org/10.5772/intechopen.75620

111

 Dynamic 3.02 2.85 3.04 3.09 3.15 Informative 3.1 3.3 3.4 3.29 2.9 Simple 2.98 3.14 3.05 2.96 3.01 Bright 2.96 2.84 3.02 3.02 3.14 Harmony 2.91 2.83 2.9 3.01 3.27 Comfort 3.14 2.74 3.06 3.17 3.03 Rigid 3.21 3.03 3.0 2.79 2.9 Unique 3.13 3.2 2.81 2.85 3.08 Passion 3.13 2.93 2.92 2.73 3.01 Formal 3.03 3.0 3.03 2.98 2.91

**Figure 5** shows the result of analysis using principal component vector (PCV) analysis. PCV is used to visualize direction and strength of emotion over the structure of emotion, to determine Kansei area [6]. It shows the distribution of e-Learning systems according to learners'

source e-Learning systems.

**Table 3.** Kansei word relationship.

**No. Kansei words Specimens**

**Table 2.** Average data from all participants.

**Kansei words**

#### **3.3. Principal component analysis**

**Table 4** and **Figure 4** show the result of principal component analysis. According to data collection, there are many factors that have significant impact to the specimens. It provides evidence


**Table 2.** Average data from all participants.

toward the specimens of e-Learning system. Each specimen is shown for a limited time one by one in an experimental room; all participants rate their Kansei responses to fill the score (from

**Moodle Efront Opigno Chamilo ATutor**

Multivariate analysis is performed to analyze the average data obtained from the evaluation session. Existing software statistic such as XLStat or SPSS can be used for calculating questionnaire data. Finally, this study provides a recommendation of the desired open source

In this study, questionnaire data from all participants are collected and then calculated its

The data is analyzed by two kinds of multivariate analysis: principal component analysis for analyzing distribution of Kansei words and specimens and factor analysis for exploring the

The relationship between emotions represented by Kansei words is shown in **Table 3**. According to this result, Kansei word relationship can be divided into six categories such as very strong, strong, enough, weak, very weak, and no relationship. For example, the emotion of bright has

**Table 4** and **Figure 4** show the result of principal component analysis. According to data collection, there are many factors that have significant impact to the specimens. It provides evidence

strong relationship with the emotion of dynamic, but has no relationship with simple.

1 to 5) of every Kansei word into the Kansei checklist.

**No. Kansei words Specimens**

1 Dynamic 2 Informative 3 Simple 4 Bright 5 Harmony 6 Comfort 7 Rigid 8 Unique 9 Passion 10 Formal

110 Trends in E-learning

**3.1. Data collection**

biggest emotion.

average as shown in **Table 2**.

**3.2. Coefficient correlation analysis**

**3.3. Principal component analysis**

e-Learning system to be used in supporting learning process.

**Table 1.** Questionnaire using Kansei words and five-point scale semantic differential.


**Table 3.** Kansei word relationship.

of the variability for each D1 to D4. The level of variability of D1 is 47.771%; D2 is 29.407%, respectively. The total cumulative value of D1 and D2 is 77.178% more than threshold value of 70%. It means that these two factors of D1 and D2 can be used for further analysis because these factors have enough influence for representing learners' emotions toward the five open source e-Learning systems.

**Figure 5** shows the result of analysis using principal component vector (PCV) analysis. PCV is used to visualize direction and strength of emotion over the structure of emotion, to determine Kansei area [6]. It shows the distribution of e-Learning systems according to learners'


**Table 4.** Percentage of variance.

**Figure 4.** Factors from principal component analysis.

emotions. Evidence showed in **Figure 5** that specimen ATutor, which is found residing in the positive x and y axes, is nearest to emotion of harmony. On the other hand, Chamilo is somewhat near to emotion of comfort, Opigno is near to emotion of informative, Efront is near to emotion of simple, and Moodle is mostly near to rigid.

### **3.4. Factor analysis**

This analysis is to refine the result of principal component analysis. Varimax rotation is used in this analysis to generate more accurate result. **Table 5** shows the result of this analysis. There are two factors with contribution level, respectively, with Factor 1 of 46.838% and Factor 2 of 29.050%. This means that Factor 1 has the highest score of contribution. In cumulative percentage, Factor 1 and Factor 2 have represented 75.889% of total contribution. Analysis using these two factors is conducted to determine the coefficient of emotion and generate variability scores for each 10 Kansei words, as shown in **Tables 6** and **7**.

The factor scores shown in **Tables 6** and **7** are sorted in ascending order to determine the influence of emotion in e-Learning system. The research set the reference threshold to 0.8. Based on Factor 1, the emotions that have score of more than 0.8 are bright, dynamic, and harmony. Based on Factor 2, there is only the emotion of unique. Factor 1 and Factor 2 are represented by the emotion of harmony and the emotion of unique, respectively. The emotion that has biggest impact is harmony. Other emotions shown in **Tables 6** and **7** have value lower than 0.8, and thus it can be ignored because they have less influence to emotion in the selected open source e-Learning systems.

According to this result, it can be concluded that the emotion has influence to preferred system. The emotion or psychological aspect should be considered in selecting the open source e-Learning system which is harmony and as alternative emotions which are unique, dynamic, and bright. Academic institution should recommend developer to give full attention to these

**Factor 1 Factor 2**

Analysis Learners' Preference in E-Learning System Using Kansei Approach

http://dx.doi.org/10.5772/intechopen.75620

113

emotions when designing interface of e-Learning system.

**Table 6.** Emotion impact priority based on Factor 1.

Variability (%) 46.838 29.050 Cumulative (%) 46.838 75.889

**Kansei words Factor 1** Informative −0.819 Formal −0.787 Simple −0.628 Rigid −0.376 Unique −0.093 Passion 0.128 Comfort 0.527 Bright 0.896 Dynamic 0.903 Harmony 0.968

**Figure 5.** Principal component vector.

**Table 5.** Factor analysis.

Analysis Learners' Preference in E-Learning System Using Kansei Approach http://dx.doi.org/10.5772/intechopen.75620 113

**Figure 5.** Principal component vector.


**Table 5.** Factor analysis.

emotions. Evidence showed in **Figure 5** that specimen ATutor, which is found residing in the positive x and y axes, is nearest to emotion of harmony. On the other hand, Chamilo is somewhat near to emotion of comfort, Opigno is near to emotion of informative, Efront is near to

**D1 D2 D3 D4**

Variability (%) 47.771 29.407 18.679 4.143 Cumulative (%) 47.771 77.178 95.857 100.000

This analysis is to refine the result of principal component analysis. Varimax rotation is used in this analysis to generate more accurate result. **Table 5** shows the result of this analysis. There are two factors with contribution level, respectively, with Factor 1 of 46.838% and Factor 2 of 29.050%. This means that Factor 1 has the highest score of contribution. In cumulative percentage, Factor 1 and Factor 2 have represented 75.889% of total contribution. Analysis using these two factors is conducted to determine the coefficient of emotion and generate variability

The factor scores shown in **Tables 6** and **7** are sorted in ascending order to determine the influence of emotion in e-Learning system. The research set the reference threshold to 0.8. Based on Factor 1, the emotions that have score of more than 0.8 are bright, dynamic, and harmony. Based on Factor 2, there is only the emotion of unique. Factor 1 and Factor 2 are represented by the emotion of harmony and the emotion of unique, respectively. The emotion that has biggest impact is harmony. Other emotions shown in **Tables 6** and **7** have value lower than 0.8, and thus it can be ignored because they have less influence to emotion in the selected open source e-Learning systems.

According to this result, it can be concluded that the emotion has influence to preferred system. The emotion or psychological aspect should be considered in selecting the open source

emotion of simple, and Moodle is mostly near to rigid.

**Figure 4.** Factors from principal component analysis.

scores for each 10 Kansei words, as shown in **Tables 6** and **7**.

**3.4. Factor analysis**

**Table 4.** Percentage of variance.

112 Trends in E-learning


**Table 6.** Emotion impact priority based on Factor 1.

e-Learning system which is harmony and as alternative emotions which are unique, dynamic, and bright. Academic institution should recommend developer to give full attention to these emotions when designing interface of e-Learning system.

#### 114 Trends in E-learning


**Variable Coefficient Range Impact**

Analysis Learners' Preference in E-Learning System Using Kansei Approach

http://dx.doi.org/10.5772/intechopen.75620

115

BGColorGray 0.0974 0.1948 √

BodyFont12 −0.0482 0.0964 √

BodyFontCalibri −0.0050 0.0100 –

HeaderColorBlack −0.0712 0.1136 √ HeaderLogoOK 0.0000 0.0000 – HeaderImgOK 0.0050 0.0050 –

TopMenuBGColBlack 0.0424 0.0906 –

TopMenuFontMedium −0.0689 0.1113 √

TopMenuBelowHeader −0.0689 0.1113 √

BodyMenuCenter 0.0349 0.0977 √

BodyMenuIcon 0.0974 0.1948 √

BodyMenuTextMedium 0.0349 0.0698 –

SearchBarAsTextbox 0.0628 0.0863 –

SearchBarAtTopRight 0.0974 0.1686 √

Average of range 0.0964

BGColorWhite −0.0974

BodyFont10 0.0482

BodyFontArial 0.0050

HeaderColorWhite −0.0050 HeaderColorGray 0.0349 HeaderColorGreen 0.0424

TopMenuBGColGreen −0.0482 TopMenuBGColGray −0.0296 TopMenuBGColWhite 0.0000

TopMenuFontSmall 0.0424

TopMenuAboveHeader 0.0424

BodyMenuRight −0.0238 BodyMenuLeft −0.0628

BodyMenuText −0.0974

BodyMenuTextSmall −0.0349

SearchBarAsLink −0.0235

SearchBarAtTopCenter −0.0712

**Table 9.** The impact of design elements.

**Table 7.** Emotion impact priority based on Factor 2.

#### **3.5. Partial least square**

The evaluation using partial least square needs two kinds of data such as average data of questionnaire and all specimens' design elements. As shown in **Table 8**, data of design elements consists of each specimen's design elements in a form of table with value of 0 and 1; if a specimen has the element, it will be set as 1; if a specimen has no element, it will be set 0.

Partial least square combines these two kinds of data to generate data of design elements based on learners' emotion as shown in **Table 9**. Comprehensive investigation can be implemented in order to determine link between emotion and design elements.

The results of this analysis are what kind of design elements should be considered when designing interface of e-Learning based on learners' emotion. The biggest emotion evaluated by factor analysis will be the critical point of design elements to be considered. In this case, factor analysis's result shows that the emotion of harmony has greatest impact in designing an interface of e-Learning.


**Table 8.** Design elements of specimens.


**Table 9.** The impact of design elements.

**3.5. Partial least square**

114 Trends in E-learning

**Table 7.** Emotion impact priority based on Factor 2.

an interface of e-Learning.

**Table 8.** Design elements of specimens.

**No. Kansei words**

The evaluation using partial least square needs two kinds of data such as average data of questionnaire and all specimens' design elements. As shown in **Table 8**, data of design elements consists of each specimen's design elements in a form of table with value of 0 and 1; if a specimen has the element, it will be set as 1; if a specimen has no element, it will be set 0.

Partial least square combines these two kinds of data to generate data of design elements based on learners' emotion as shown in **Table 9**. Comprehensive investigation can be imple-

The results of this analysis are what kind of design elements should be considered when designing interface of e-Learning based on learners' emotion. The biggest emotion evaluated by factor analysis will be the critical point of design elements to be considered. In this case, factor analysis's result shows that the emotion of harmony has greatest impact in designing

 Moodle 1 0 0 1 1 … Efront 0 1 0 1 1 … Opigno 0 1 0 1 0 … Chamilo 1 0 1 0 1 … ATutor 1 0 0 1 1 …

**BGColorWhite BGColorGray BodyFont10 BodyFont12 HeaderColorWhite …**

mented in order to determine link between emotion and design elements.

**Kansei words Factor 2** Informative −0.570 Comfort −0.540 Dynamic −0.385 Bright −0.270 Formal −0.136 Harmony 0.028 Simple 0.402 Rigid 0.658 Passion 0.795 Unique 0.907

#### **3.6. Recommendation of design elements**

For further analysis to support interface design, it needs to calculate the importance of each design element, using procedure as follows [12]:

The proposed method in this study can be used for more specific object, for example, the content of learning material, in order to provide the proper learning material based on learners'

Analysis Learners' Preference in E-Learning System Using Kansei Approach

http://dx.doi.org/10.5772/intechopen.75620

117

Further study is proposed to investigate e-Learning system using Kansei engineering based on wider population and demography, to investigate e-Learning interface design elements in more detail. It is critical to explore design element more detail in order to enhance open

The author would like to thank to STMIK LIKMI Bandung, Malaysia Association of Kansei Engineering (MAKE), UiTM, and P2I-LIPI for their great supporting in Kansei Research.

[1] Lorenzo M, Emanuele B. Abduction and web interface design. In: Ghaoui C, editor. Encyclopedia of Human Computer Interaction. Hershey: Ide Group Reference; 2006

[2] Lokman AM, Ishak KK, Razak FHA, Aziz AA. The feasibility of PrEmo in cross-cultural Kansei measurement. In: IEEE Symposium on Humanities, Science and Engineering

[3] Sato N, Anse M, Tabe T. A method for constructing a movie-selection support system based on Kansei engineering. In: The 12th International Conference on Human-

[4] Kodai T, Onisawa T. Generation of scene frame of Manga from narrative text. In: International Conference on Kansei Engineering and Emotion Research (KEER); Paris; 2010

[5] Ismail MN, Lokman AM, Abdullah NAS. Formulating Kansei concepts of assistive device for people with physical disabilities. In: 3rd International Conference on User

[6] Lokman AM. Emotional user experience in web design: The Kansei engineering approach (A PhD Thesis); 2009. Retrieved 20 January 2015 from: http://www.anitawati.

source platform of e-Learning system based on learners' emotion.

Address all correspondence to: anahadiana68@gmail.com

Research; 2012. pp. 1033-1038

uitm.edu.my

Computer Interaction (HCII); Beijing; 2007

Science and Engineering (i-USEr); Shah Alam; 2014

Research Center for Informatics (P2I)-LIPI, STMIK-LIKMI, Bandung, Indonesia

psychological aspects.

**Acknowledgements**

**Author details**

Ana Hadiana

**References**


According to the result shown in **Table 9**, the emotion of harmony-based design elements of interface of e-Learning is focused on element that has high impact. E-Learning's interface is recommended as follows:


Other elements which have low impact are still considered as alternative design element; it can be changed with different values.
