In this paper, we describe an automatic personalization approach aiming to provide online automatic recommendations for active learners without requiring explicit feedback. Recommended learning resources are computed based on the current learner’s recent navigation history, as well as exploiting similarities and dissimilarities among learners’ preferences and educational content. The proposed framework for building automatic recommendations in e-learning platforms is composed of two modules: an off-line module which preprocesses data to build learner and content models, and an online module which uses these models on-the-fly to recognize the students’ needs and goals, and predict a recommendation list. Recommended learning objects are obtained by using a range of recommendation strategies based mainly on content-based filtering and collaborative filtering approaches, each applied separately or in combination.
E-learning, Automatic Personalization, Recommender Systems, Content-based filtering, Collaborative Filtering
Up to the very recent years, most e-learning systems have not been personalized. Several works have addressed the need for personalization in the e-learning domain. However, even today, personalization systems are still mostly confined to research labs, and most of the current e-learning platforms are still delivering the same educational resources in the same way to learners with different profiles. In general, to enable personalization, existing systems used one or more types of knowledge (learners’ knowledge, learning material knowledge, learning process knowledge, etc). Generally, personalization in e-learning systems concerns: adaptive interaction, adaptive course delivery, content discovery and assembly, and adaptive collaboration support. The category of adaptive course delivery represents the most common and widely used collection of adaptation techniques applied in e-learning systems today. Typical examples include dynamic course re-structuring and adaptive selection of learning objects, as well as adaptive navigation support, which has all benefited from the rise of using recommendation strategies to generate new and relevant links and items. In fact, one of the new forms of personalization in the e-learning environment is to give recommendations to learners in order to support and help them through the e-learning process.
A number of personalized systems have relied on explicit information given by a learner (demographic, questionnaire, etc) and have applied known methods and techniques of adapting the presentation and navigation (Chorfi et al., 2004). As explained in (Brusilovsky, 1996), two different classes of adaptation can be considered: adaptive presentation and adaptive navigation support. Later, in (Brusilovsky, 2001), the taxonomy of adaptive hypermedia technologies was updated to add some extensions in relation to new technologies. Then, the distinction between two modes of adaptive navigation support became a necessity, especially with the growth of recommender systems. Automatic recommendation implies that the user profiles are created and eventually maintained dynamically by the system without explicit user information. Examples include amazon.com’s personalized recommendations and music recommenders like Mystrand.com in commercial systems (Mobasher 2006), smart recommenders in e-learning (Zaiane, 2002), etc. In general, such systems differ in the input data, in user modeling strategies, and in prediction techniques. Several approaches for automatic personalization have been reported in the literature, such as content-based or item-based filtering, collaborative filtering, rule-based filtering, and techniques relying on Web usage mining, etc (Nasraoui, 2005). Web recommender systems can be categorized depending on these approaches. Content-based filtering (or item-based filtering) systems recommend items to a given user based on the correlation between the content of these items and the preferences of the user (Meteren et al., 2000). This means that the recommended items are considered to be similar to those seen and liked by the same user in the past. Thus, there is no notion of a community of users, rather only one user profile is considered while making recommendations. Classical examples of systems applying a content-based filtering approach include among other Personal webwatcher (Mladenic , 1996), syskill and webert (Pazzani et al., 1997), etc.
The collaborative filtering system recommends items that are liked by other users with similar interests. Thus, the exploration of new items is assured by the fact that other similar user profiles are also considered. Examples of such systems include GroupLens (Konstan et al., 1997) and (Sarwar et al., 1998). Hybrid recommender systems combine several recommendation strategies to provide better performance than either strategy alone. Most hybrids work by combining several input data sources or several recommendation strategies. There are many hybridization methods reported in the state of the art, content/collaborative hybrids are the most popular hybrid strategies. Generally, web recommender systems tend to use web mining techniques in one or more stages of the recommendation process. In e-learning, the interest in using web mining has recently increased, especially with the rapid spread of web-based learning environments in education and the growing need to give personalized services to students. In e-learning systems, web mining techniques are used to learn all available information about learners and build models to apply in personalization. A detailed description of using and applying educational data mining was given in (Romero et al., 2006) and (Romero et al., 2007). Several techniques could be used for personalization and recommendation such as classification, clustering, prediction, association rule, and sequential pattern. In (Tiffany et al., 2003), students were clustered with similar learning characteristics and applied collaborative filtering to provide a paper recommendation.
A recommender agent using association rules has been used to recommend e-learning activities in (Zaiane, 2002). Providing to a student the next link or task to do within the adaptable educational hypermedia system AHA! was the aim of the recommender system described in (Romero et al., 2007). In this paper, we are going to describe an automatic personalization approach for providing learning object recommendations for online students in e-learning systems. It is to be noted here that by adopting the term “Learning Object” we mean any digital educational resource used within the e-learning environment, it could be a course, a web page, a simulation, i.e. all known formats of digital educational resources regardless their granularity. These learning objects are referenced within the e-learning system by their URL, they are also archived in log files or tracked in databases as URL references. Therefore, the automatic recommendation of learning objects means generating a list of URLs referencing educational resources (hosted and/or created inside the e-learning platform) in order to guide and support the e-learners. The proposed approach is taking into account both the Web access history of learners as well as the content of the learning material, Web mining techniques in combination with an open-source Web information retrieval system are used to enable an implementation that is not only open and scalable but also fast to deploy. The recommender system we aim to develop should be considered as an external module or plug-in that can be included easily in e-learning systems (courseware, LMS, etc) to give automatic personalization. This paper is arranged in the following way: first, we describe the proposed approach and the corresponding phases of modeling and recommending. In Section 3, we present some implementations of the proposed methodologies. In Section 4, we make some experiments and evaluations. Finally, conclusions and future work are presented.
Conclusions and future work
In this paper, we have outlined the general principles of a new approach to perform personalization in e-learning platforms by resorting to a recommender system relying on web mining techniques and scalable search engine technology to take care of one of the crucial steps in personalization, which occurs in the “online” phase to compute the recommendations against a possibly massive repository of educational resources in “real-time”. In the modeling phase, we used Nutch’s automated crawling and indexing techniques as well as standardized educational content metadata to build content models, and Web usage mining techniques (clustering and association rule mining) to build learner profiles. Hybrid recommendations (based on CBF and CF) were used in the recommendation phase. We are currently exploring several techniques and strategies in the modeling and recommendation phase in more detail, and performing more evaluations. We are also studying the possibility of integrating educational preferences in the learner’s model such as learning styles, media types, etc. The learner’s model to consider, in the future work, should be composed of three main components: learner’s profile, learner’s knowledge, and learner’s educational preferences. All these components should be detected automatically within e-learning systems. After the construction of the student models, we build group models using a three-level collaborative modeling approach. We expect this enrichment of the learner’s model to increase the quality of learning object recommendations especially from an instructional point of view.
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FULL Paper PDF file:Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval
Mohamed Koutheaïr Khribi1, Mohamed Jemni1 1 Technologies of Information and Communication Lab, Higher School of Sciences and Technologies of Tunis, University of Tunis, Tunisia // firstname.lastname@example.org // email@example.com
Olfa Nasraoui, Knowledge Discovery & Web Mining Lab, University of Louisville, USA // firstname.lastname@example.org
Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval