Book ChapterDOI
Panorama of Recommender Systems to Support Learning
Hendrik Drachsler,Katrien Verbert,Katrien Verbert,Olga C. Santos,Nikos Manouselis +4 more
- pp 421-451
TLDR
In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework and analysed for their contribution to the evolution of the RecSysTEL research field.Abstract:
This chapter presents an analysis of recommender systems in Technology-Enhanced Learning along their 15 years existence (2000–2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into seven clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.read more
Citations
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Journal ArticleDOI
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning
TL;DR: This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations and hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e- learning recommenders.
Journal ArticleDOI
Review of Research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems
Robert Bodily,Katrien Verbert +1 more
TL;DR: Based on this review, there needs research on learning analytics reporting systems that targets the design and development process of reporting systems, not only the final products.
Journal ArticleDOI
Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey
TL;DR: This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.
Proceedings ArticleDOI
Trends and issues in student-facing learning analytics reporting systems research
Robert Bodily,Katrien Verbert +1 more
TL;DR: A literature review on systems that track learning analytics data and provide a report back to students in the form of visualizations, feedback, or recommendations is conducted to identify trends in the current student-facing learning analytics reporting system literature.
Journal ArticleDOI
An e-learning recommendation approach based on the self-organization of learning resource
TL;DR: This study incorporates an LO-oriented recommendation mechanism to learner-oriented recommender systems, and proposes an LO self-organization based recommendation approach (Self), which demonstrates the high adaptability, diversity, and personalization of the recommendations.
References
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Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Journal ArticleDOI
Hybrid Recommender Systems: Survey and Experiments
TL;DR: This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.
Journal ArticleDOI
E-Commerce Recommendation Applications
TL;DR: An explanation of how recommender systems are related to some traditional database analysis techniques is presented, and a taxonomy ofRecommender systems is created, including the inputs required from the consumers, the additional knowledge required from a database, the ways the recommendations are presented to consumers,The technologies used to create the recommendations, and the level of personalization of the recommendations.
Journal ArticleDOI
A Taxonomy of Recommender Agents on theInternet
TL;DR: A state-of-the-art taxonomy of intelligent recommender agents on the Internet and a cross-dimensional analysis with the aim of providing a starting point for researchers to construct their own recommender system.