**3. E-learning recommender systems**

Today, e-business applications and e-services are commonly taking advantage of advanced information and communication technology and methodologies to personalize their interactions with users. Personalization aims to tailor services to individual needs, and its immediate objectives are to understand and to deliver highly focused, relevant content, services and products matched to users' needs and contexts (Adomavicius & Tuzhilin, 2005; Brusilovsky, 2002; Ho, 2006; Kim et al, 2009; Kim et al, 2002; Ricci & Werthner, 2006;). In eservices personalization and web adaptation have been employed in many different ways: (i) the personalization service can be designed and used as an advice-giving system to provide recommendations to each individual and to generate up-sell and cross-sell opportunities (ii) personalization services are used to (dynamically) structure the index of information, product pages based on click-stream analysis to minimize the users' search efforts, where personalized content based on the user's profile is generated. The users can personalize not only the content but also the interface of the application used (Brusilovsky & Maybury, 2002; Burke, 2000; Rashid, 2002 ).

Applications of personalization technology are found to be useful in different domains. These include information dissemination, entertainment recommendations, search engines, medicine, tourism, financial services, consumer goods and e-learning (Adomavicius & Tuzhilin, 2005; Garcia-Crespo et al, 2011; Kim et al, 2009; Papanikolaou, & Grigoriadou, 2002).

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

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

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

Today, e-business applications and e-services are commonly taking advantage of advanced information and communication technology and methodologies to personalize their interactions with users. Personalization aims to tailor services to individual needs, and its immediate objectives are to understand and to deliver highly focused, relevant content, services and products matched to users' needs and contexts (Adomavicius & Tuzhilin, 2005; Brusilovsky, 2002; Ho, 2006; Kim et al, 2009; Kim et al, 2002; Ricci & Werthner, 2006;). In eservices personalization and web adaptation have been employed in many different ways: (i) the personalization service can be designed and used as an advice-giving system to provide recommendations to each individual and to generate up-sell and cross-sell opportunities (ii) personalization services are used to (dynamically) structure the index of information, product pages based on click-stream analysis to minimize the users' search efforts, where personalized content based on the user's profile is generated. The users can personalize not only the content but also the interface of the application used (Brusilovsky &

Applications of personalization technology are found to be useful in different domains. These include information dissemination, entertainment recommendations, search engines, medicine, tourism, financial services, consumer goods and e-learning (Adomavicius & Tuzhilin, 2005; Garcia-Crespo et al, 2011; Kim et al, 2009; Papanikolaou,

of the recommender system.

material: textbooks, notes, videos, and VR cases.

**3. E-learning recommender systems** 

Maybury, 2002; Burke, 2000; Rashid, 2002 ).

& Grigoriadou, 2002).

and user-friendly information technologies emphasizing on VR tools.

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

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).

Fig. 2. Adaptive e-Learning System

Development of an e-Learning Recommender System

Fig. 3. E-learning Recommender Methodological Framework

constraints.

for each job position.

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

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

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

Using Discrete Choice Models and Bayesian Theory: A Pilot Case in the Shipping Industry 41

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 intelligent technologies for user modelling and adaptation.

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.

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 performance of the recommender system itself .
