**2. The context**

The development of the presented intelligent training advisor, took place within the scope of the SLIM-VRT E.U. research project and was applied in the shipping industry. A field study was conducted for requirements data collection, with 5000 questionnaires and 24% response rate. The techniques used offer the advantage of a comparatively detailed, userfocused e-learning attributes modelling framework (advanced choice theory) and a competent system learning capability (Bayesian theory), that improves over time the performance of the recommender system itself .

In the following, we explain development of the advisor system in the context of involved and affected stakeholders, as shown in Fig.1, including:


These groups interact in a common environment and the actions of one group directly affect the other. Obviously, these major groups of stakeholders in a particular market, for instance the shipping industry, operate in a distinct, dynamic Economic, Technological and Legislative environment.

Τhe methodology we propose is implemented in the shipping industry, where seafarers have specific learning needs in order to adapt and perform successfully in a continuously changing organizational, business, and employment environment (Theotokas & Progoulaki 2007; Progoulaki & Theotokas, 2008; Progoulaki et al, 2005).

Common career paths in the shipping industry may require alternate career changes and job rotation such as acquisition of different positions onboard, work on different types of ships, and employment, ashore, at an office position related with the shipping and port operations management. The training process should take into account particularities of the seafaring profession such as having long intervals between two employment periods, being far away from the family when employed onboard, experiencing alienation form the broader society, etc. Thus, the context of the maritime e-learning is highlighted by those specific characteristics underpinning the shipping market and educational environment. Today, the following features are pertinent to the shipping workforce training aspects:


This chapter presents an innovative methodology for the development of a training advisor for e-learning environments. We consider e-learning personalization issues and present an e-learning recommender framework based on discrete choice models and Bayesian theory

The development of the presented intelligent training advisor, took place within the scope of the SLIM-VRT E.U. research project and was applied in the shipping industry. A field study was conducted for requirements data collection, with 5000 questionnaires and 24% response rate. The techniques used offer the advantage of a comparatively detailed, userfocused e-learning attributes modelling framework (advanced choice theory) and a competent system learning capability (Bayesian theory), that improves over time the

In the following, we explain development of the advisor system in the context of involved

These groups interact in a common environment and the actions of one group directly affect the other. Obviously, these major groups of stakeholders in a particular market, for instance the shipping industry, operate in a distinct, dynamic Economic, Technological and

Τhe methodology we propose is implemented in the shipping industry, where seafarers have specific learning needs in order to adapt and perform successfully in a continuously changing organizational, business, and employment environment (Theotokas & Progoulaki

Common career paths in the shipping industry may require alternate career changes and job rotation such as acquisition of different positions onboard, work on different types of ships, and employment, ashore, at an office position related with the shipping and port operations management. The training process should take into account particularities of the seafaring profession such as having long intervals between two employment periods, being far away from the family when employed onboard, experiencing alienation form the broader society, etc. Thus, the context of the maritime e-learning is highlighted by those specific characteristics underpinning the shipping market and educational environment. Today, the

There is a decline of interest in the seafaring profession and shortage of ex-ship officers

The impact of new technologies in terms of reorganization of crew duties is important

The safety of life at sea and the protection of marine environment have been a basic

(Chaptini, 2005; Greene, 1993).

performance of the recommender system itself .

and affected stakeholders, as shown in Fig.1, including:

2007; Progoulaki & Theotokas, 2008; Progoulaki et al, 2005).

following features are pertinent to the shipping workforce training aspects:

**2. The context** 

1. the training society;

Legislative environment.

2. the firms and organizations; 3. the learners/trainees or end-users;

for shore-based positions

Maritime personnel needs to be polyvalent

concern of the maritime community

Fig. 1. Stakeholders in an e-learning Environment


To address these needs in a systematic and valid manner we firstly developed a tool for accumulating the basic knowledge regarding the seafaring profession and training environment, registered the information of the interviewed sample population during the data collection phase, and used statistical analysis to support the choices of each individual separately.

Development of an e-Learning Recommender System

(Balabanovic, 1998; Lee, 2001; Sarwar, 2000).

Fig. 2. Adaptive e-Learning System

Using Discrete Choice Models and Bayesian Theory: A Pilot Case in the Shipping Industry 39

According to (Papanikolaou, & Grigoriadou, 2002) Adaptive Educational Hypermedia Systems aim to increase the functionality of hypermedia by making it personalised to individual learners. The adaptive dimension of these systems mainly refers to the adaptation of the content or the appearance of hypermedia to the knowledge level, goals and other characteristics of each learner. Learners' knowledge level and individual traits are used as valuable information to represent learners' current state and personalise the educational system accordingly, in order to facilitate learners to achieve their personal learning goals and objectives. Nowadays, most e-learning recommender systems consider learner/user preferences, interests, and browsing behaviours when analyzing personalization considering different levels of learner/user knowledge A distinctive feature of an adaptive e-learning system is a comprehensive user model that represents user knowledge, learning goals, interests, and pertinent contextual features that enable the system to distinguish among different user groups and feasible learning service solutions

Over the last 10 years, researchers in adaptive hypermedia and Web systems have explored many user modelling and adaptation methods, whereas a number of them already have been applied to the e-learning domain. The pre-Web generation of adaptive hypermedia systems explored mainly adaptive presentation and adaptive navigation support and concentrated on modelling user knowledge and goals. Empirical studies have shown adaptive navigation support can increase the speed of navigation and learning, whereas adaptive presentation can improve content understanding. The Web generation emphasized exploring adaptive content selection and adaptive recommendation based on modelling user interests. The mobile generation is now extending the basis of the adaptation by adding models of context and

situation-awareness (location, time, computing platform, quality of service).

Four major groups of potential e-learning users are identified: a) Junior and Senior Captains; b) Junior and Senior Engineers; c) Marine students and d) Office personnel. Each group has special characteristics, interests and needs with regard to their career perspectives (continuous employment onboard, get promoted or switch career from the ship to the office), and respective training needs, which are specifically taken into account in the design of the recommender system.

Seafarers' individual preferences for course attributes and learning methodologies vary. The overall learning methodology they favour should comprise the following characteristics to meet the user needs: (a) Adaptive-blended learning (combined traditional lectures, elearning in the office, e-learning on board with instructor); (b) Cooperative learning (practice on board, emergency drills with peers); (c) Contextual and participatory learning (via simulation, or Virtual Reality (VR) case studies); and (d) Provision of a variety of training material: textbooks, notes, videos, and VR cases.

In order to demonstrate the particular framework proposed hereafter, we developed an elearning platform with training advisor capabilities for the shipping sector entitled SLIM-VRT. SLIM-VRT enables the existing and potential maritime students, employees, employers and authorities in the shipping industry to receive training in a way that is userfriendly, flexible, and learning effective. The overall SLIM-VRT system presents three major innovative dimensions: a) dynamic education program and content generation according to the user individual needs, especially in terms of the training advisor recommender capabilities, as explained hereafter; b) new pedagogical methodologies emphasizing collaborative and contextual learning, on the basis of case studies; and c) use of innovative and user-friendly information technologies emphasizing on VR tools.
