Academic journal article Educational Technology & Society

Effective Trust-Aware E-Learning Recommender System Based on Learning Styles and Knowledge Levels

Academic journal article Educational Technology & Society

Effective Trust-Aware E-Learning Recommender System Based on Learning Styles and Knowledge Levels

Article excerpt

Introduction

Due to unprecedented proliferation of information and communication technologies in recent years, e-learning has become more and more popular in academics as well as in commercial environments (Zaiane, 2002). E-learning provides opportunities for learners not only to study courses or to learn professional knowledge without time and space constraints, but also to train themselves at their own pace through the asynchronous and synchronous learning network models (Chao and Chen, 2009). Due to enormous amount of learning resources in e-learning environment, learners face difficulties in searching appropriate resources according to their need (Ghauth & Abdullah, 2010). In this situation, recommender system (RS) seems a proficient solution for dealing with this resource overload in elearning environment (Khribi et al., 2009).

Recommender systems (RSs) are one of the most promising technologies of web personalization to alleviate the problem of information and product overload. They provide personal, affordable and effective recommendations to users based on their preferences expressed, either explicitly or implicitly (Adomavicius & Tuzhilin, 2005; Al-shamri & Bharadwaj, 2008; Milicevic et al., 2010). E-learning recommender systems (ELRSs) deal with information about learners and their learning activities and recommend items such as articles, web pages, etc. (Nghe et al., 2010). Collaborative filtering provides recommendations to learner based on those learners who have similar preferences. Since CF is able to capture the particular preferences of a user so it has become most widely accepted technique in RSs for recommending web pages, music, books etc(Symeonidis et al., 2008). It has also been successfully employed in ELRSs (Manouselis et al., 2010; Bobadilla et al., 2009; Dwivedi & Bharadwaj, 2011).

RS is strongly context/domain dependent, so it is not feasible that recommendation strategy for one context/domain is transferable to others (Drachsler et al., 2007). The reason why the thriving application of movie or joke recommendation strategies has not had such an efficacy in e-learning because modeling accurate learner profile is a much harder task than in other application domains. Two important open research issues in ELRSs are as follows:

* Learner's point of view: Recommended resources should be interesting to learners, according to their needs as well as their characteristics.

* Designer's point of view: How to design learning materials considering learners' preferences and how to recommend these resources in a specified sequence so that learners' performance can be enhanced.

We designed our proposed ELRSs based on learner's point of view by taking into consideration learner's characteristics namely learning styles and knowledge levels etc.

In e-learning, learners are characterized on the basis of their learning styles, emotions, knowledge levels and goals etc. (Drachsler et al., 2007). Learning style of a learner can be considered as a valuable factor for enhancing the individual learning that would affect the recommendation task. Learning style (LS) indicates how a learner learns and likes to learn. It can be analyzed or collected from the learning behavior of learner during study (Chang et al., 2009; Garcia et al., 2008). Bobadilla et al. (2009) suggested that learners with greater knowledge should have greater weight in the computation of recommendation than the learners with less knowledge among all neighbors of an active learner in collaborative filtering framework. Therefore knowledge level of learners is an important factor in addition to LSs. Therefore, we are providing a hybrid ELRS which offers resource recommendations by acclimatizing automatically learners' learning styles and knowledge levels that would favor and improve the learning.

The following assumptions that motivated us for the adaptation of learning style and knowledge level in ELRS are:

* Learners with different learning style generate different perspectives on effective strategies for dynamic group interactivity (Kolb, 1976). …

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