**4. Training advisor development: A pilot case in the shipping industry**

As mentioned above, the particular training advisor framework proposed hereafter, was developed as integrated with the SLIM-VRT e-learning platform for the shipping sector.

#### **4.1 Methodological framework**

The training advisor we developed is primarily characterized by a detailed, user-focused modelling approach, as denoted in Fig. 3.The user model that is the employees' preferences for e-learning is a function of (a) the Trainees Profile, which includes their socioeconomic characteristics, educational profile, working experience, and learning profile; and (b) The Training Package characteristics. Each training package is composed of one or more courses with specific attributes such as training method, duration of training, location of training, assessment method, training material, training institution, and training cost.

Individuals' preferences with regards to the training package are formed based on their training objectives and career expectations. That is, individuals may want to be promoted, change career, or stay in the current position, but enhance certain skills or improve their

An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user (Brusilovsky & Maybury, 2002). The user model is used to provide an adaptation effect, that is, tailor interaction to different users in the same context. Adaptive systems often use

In specific, user modelling could be considered as the key process that enables recommendation systems to generate offers to users according to their needs, using contentbased, filtering based and hybrid techniques (Adomavicius & Tuzhilin, 2005). More analytically, in the case of recommender system based on an intelligent system, the recommendation process is commonly implemented by a rule-based system that maintains a collection of knowledge facts, also denoted as the knowledge base (registering attributes such as student's preferences, interests and knowledge levels). The knowledge of an intelligent system can be expressed in several ways. One common method is in the form of "if-then-else" type rules. A simple comparison of the input to a set of rules will implement the desired actions, in order to deduct the recommendation. An intelligent system platform can be utilized for an e-learning recommendation system, such as the Java Expert System Shell (JESS), which represents its knowledge not only in the form of rules but also as objects.

This allows rules to use pattern matching on the fact objects as well as input data.

**4. Training advisor development: A pilot case in the shipping industry** 

assessment method, training material, training institution, and training cost.

As mentioned above, the particular training advisor framework proposed hereafter, was developed as integrated with the SLIM-VRT e-learning platform for the shipping sector.

The training advisor we developed is primarily characterized by a detailed, user-focused modelling approach, as denoted in Fig. 3.The user model that is the employees' preferences for e-learning is a function of (a) the Trainees Profile, which includes their socioeconomic characteristics, educational profile, working experience, and learning profile; and (b) The Training Package characteristics. Each training package is composed of one or more courses with specific attributes such as training method, duration of training, location of training,

Individuals' preferences with regards to the training package are formed based on their training objectives and career expectations. That is, individuals may want to be promoted, change career, or stay in the current position, but enhance certain skills or improve their

In the following, we present a particular e-learning recommender framework based on advanced choice models and Bayesian techniques (Chaptini, 2005; Greene, 1993), which is considered as an intelligent recommender system. The presented e-learning recommender model is applied and tested in the shipping e-learning environment, it is though proposed for adaptation and use in different e-learning settings, also e-service environments favouring intense personalization and recommendation value-adding features. The techniques used in our system offer the strong competitive advantage of a comparatively detailed, user-focused e-learning attributes modelling framework (advanced choice theory) and a competent system learning capability (Bayesian theory), that improves over time the

intelligent technologies for user modelling and adaptation.

performance of the recommender system itself .

**4.1 Methodological framework** 

Fig. 3. E-learning Recommender Methodological Framework

knowledge on specific topics. The choice to adopt a "self-training for work" package is a function of these objectives, as well as of the resource availability and of situational constraints.

Resource availability includes training institution and course offerings, as well as job position openings by shipping companies and institutions. The situational constraints include the Employers' Requirements and the Institutional-Legal Minimum Requirements for each job position.

The underlying assumption in this recommender model framework is that employees try to choose the training package with the maximum utility. Thus, assuming a motivation to follow a "self learning for work course", they choose, among all the courses offered, the one that maximizes their utility, given their characteristics, the course attributes and the motivational and situational constraints. Given the source of motivation, employees select

Development of an e-Learning Recommender System

Fig. 4. Workflow of the Training Advisor

Using Discrete Choice Models and Bayesian Theory: A Pilot Case in the Shipping Industry 43

for consideration, from the menu of all –at present- available and non available courses, the ones that best address their needs. For example, based on the utility maximization assumption, an increase in flexibility is expected to increase the preference for the training package, while an increase in costs should decrease its preference.

Different groups of individuals are expected to have a different level of sensitivity to each of these attributes. In particular, it is expected that middle aged professional seafarers (captains and engineers) are to demonstrate higher willingness to pay for increased flexibility for courses that "lead" to shore positions. However, the extent to which an employee is willing to incur "training costs" depends on how much these costs are affected by the available income or expectations for promotion or reorientation of her/his career.

The characteristics of the job, particularly the available free time onboard may also constrain the consideration set of courses. Age, gender, marital status, profession and previous working experience are considered to have a strong effect on the perceived impact of "self training for work". For example, older learners are likely to be well-established in their career and have minimal preferences for new courses. On the other hand, young individuals (students in marine academies and personnel of shipping companies) are expected to appreciate more the flexibility and the "internet facilities" allowed by a "self learning for work" package. The training advisor developed provides as recommended training package, the one that maximises the utility of the user.

#### **4.2 Training advisor logic**

This section describes the e-learning recommender workflow implemented based on advanced choice models and Bayesian techniques for its core computational part. The proposed system architecture can be thought of as divided into two main parts according to system operation procedures, which is the front-end and back-end parts. The front-end part manages the interaction and communication with learners and records learner behavior, whereas the back-end part performs the analysis of learner preferences, skills and selects appropriate course materials for learners based on estimated learner ability. A main component of the latter subsystem is the *Training Advisor*, the logic and capabilities of which is detailed in the following section.

More specifically, our training advisor's structure and workflow can be seen as composed of 9 main steps, depicted in Figure 4.

First, at the entry of the trainee in the system a registration process is made (step 1). After the successful completion of registration, the user follows a series of steps (step 2-5) in order to receive detailed advices from the system regarding the educational package which suits in his/her needs (step 6).

In the end of the process, a trainee can review certain elements of the proposed courses (objectives, level, cost, etc.) and he/she can decide if he/she will attend one or more courses of the proposed package (step 7). Provided that the trainee has completed a full educational life cycle (step 8) this education process can be evaluated as well as the advisor usefulness (step 9).

for consideration, from the menu of all –at present- available and non available courses, the ones that best address their needs. For example, based on the utility maximization assumption, an increase in flexibility is expected to increase the preference for the training

Different groups of individuals are expected to have a different level of sensitivity to each of these attributes. In particular, it is expected that middle aged professional seafarers (captains and engineers) are to demonstrate higher willingness to pay for increased flexibility for courses that "lead" to shore positions. However, the extent to which an employee is willing to incur "training costs" depends on how much these costs are affected by the available income or expectations for promotion or reorientation of her/his

The characteristics of the job, particularly the available free time onboard may also constrain the consideration set of courses. Age, gender, marital status, profession and previous working experience are considered to have a strong effect on the perceived impact of "self training for work". For example, older learners are likely to be well-established in their career and have minimal preferences for new courses. On the other hand, young individuals (students in marine academies and personnel of shipping companies) are expected to appreciate more the flexibility and the "internet facilities" allowed by a "self learning for work" package. The training advisor developed provides as recommended training

This section describes the e-learning recommender workflow implemented based on advanced choice models and Bayesian techniques for its core computational part. The proposed system architecture can be thought of as divided into two main parts according to system operation procedures, which is the front-end and back-end parts. The front-end part manages the interaction and communication with learners and records learner behavior, whereas the back-end part performs the analysis of learner preferences, skills and selects appropriate course materials for learners based on estimated learner ability. A main component of the latter subsystem is the *Training Advisor*, the logic and capabilities of which

More specifically, our training advisor's structure and workflow can be seen as composed of

First, at the entry of the trainee in the system a registration process is made (step 1). After the successful completion of registration, the user follows a series of steps (step 2-5) in order to receive detailed advices from the system regarding the educational package which suits

In the end of the process, a trainee can review certain elements of the proposed courses (objectives, level, cost, etc.) and he/she can decide if he/she will attend one or more courses of the proposed package (step 7). Provided that the trainee has completed a full educational life cycle (step 8) this education process can be evaluated as well as the advisor usefulness

package, while an increase in costs should decrease its preference.

package, the one that maximises the utility of the user.

**4.2 Training advisor logic** 

is detailed in the following section.

9 main steps, depicted in Figure 4.

in his/her needs (step 6).

(step 9).

career.

Fig. 4. Workflow of the Training Advisor

Development of an e-Learning Recommender System

Compulsory

Governmental (Marine, Academy,

University )

(STCW/95, IMO Model Course, National Education Authority)

PROCEDURES Exams Without Exams

(Paid By Trainee)

(Institute's Quarters, In-house Training)

Classic Lectures (In Class, In Office, Onboard With Instructor)

(Textbooks, Notes,

responses to hypothetical scenarios presented to the employees.

estimate the base parameters for different trainee's profiles; and

in a relatively short time frame in order to deliver online recommendations.

Printed

Manuals)

Table 1. Attributes of the Alternative Choice

Therefore, two models are developed:

Ashore

COURSE

INSTITUTE

ASSESSMENT

LOCATION OF TRAINING

DURATION OF

TRAINING METHODOLOGY

> TRAINING MATERIAL

**5.4 Choice model** 

COST Fees

Using Discrete Choice Models and Bayesian Theory: A Pilot Case in the Shipping Industry 45

ATTRIBUTES LEVEL 1 LEVEL 2 LEVEL 3

TRAINING Academic Semester Short Courses / Seminars Flexible

Non Compulsory

Non Governmental (Helmepa, Private Training, Manufacturer)

Without Fees

Self Learning

Digital

The choice model involves the estimation of a preference function, based on the attributes presented above. The estimation is based on stated preferences data, which are expressed

With regards to the development of the training advisor it is important to estimate models

*Training Needs Module* - An offline model system: that relies on advanced choice models to

(Self Evaluation, Practice)

(Cost Paid By Government Or/And Employee)

(By Distance Or E-Learning With The Support Of Training Multimedia, Virtual Case Studies Etc)

(C.D, Diskettes, Internet, Virtual Equipment)

Onboard Ashore And

Onboard

On The Job Training (Practice Onboard, Emergency Drills, Simulation)

Audiovisual (Video,

Audiocassettes)
