Social Recommender Systems: Recommendations in Support of E-Learning

doi:10.4018/jdet.2005040103

Reference Hub

This research has been cited in:

Chapter
Survey and Analysis of TEL Recommender SystemsRecommender Systems for Learning10.1007/978-1-4614-4361-2_3
Article
An effective recommendation framework for personal learning environments using a learner preference tree and a GAIEEE Transactions on Learning Technologies10.1109/TLT.2013.28
Article
Factors Influencing Intentions to Use Social Recommender Systems: A Social Exchange PerspectiveCyberpsychology, Behavior, and Social Networking10.1089/cyber.2012.0278
Chapter
Ethics of E-Learning Recommender Systems: Epistemic Positioning and Ideological OrientationRecommender Systems: Legal and Ethical Issues10.1007/978-3-031-34804-4_11
Chapter
Strategic Decision-Making for Pedagogical Course Planning Using NLP in Social Media DataAI, Blockchain and Self-Sovereign Identity in Higher Education10.1007/978-3-031-33627-0_5
Chapter
Educational Recommender Systems: A Pedagogical-Focused PerspectiveMultimedia Services in Intelligent Environments10.1007/978-3-319-00375-7_8
Conference
Recommendation of Learning Resources and Users Using an Aggregation-Based Approach2012 Second International Workshop on Advanced Information Systems for Enterprises10.1109/IWAISE.2012.14
Chapter
Towards a Social Trust-Aware Recommender for TeachersRecommender Systems for Technology Enhanced Learning10.1007/978-1-4939-0530-0_9
Conference
Can social information retrieval enhance the discovery and reuse of digital educational content?Proceedings of the 2007 ACM conference on Recommender systems10.1145/1297231.1297276
Article
User‐centred design and educational data mining support during the recommendations elicitation process in social online learning environmentsExpert Systems10.1111/exsy.12041
Chapter
Toward a New Recommender System Based on Multi-criteria Hybrid Information FilteringComputer Science and Its Applications10.1007/978-3-319-19578-0_27
Article
Collaborative recommendation of e‐learning resources: an experimental investigationJournal of Computer Assisted Learning10.1111/j.1365-2729.2010.00362.x
Chapter
How to Set Up Simulations for Designing Light-Weight Personalised Recommender SystemsLearning Network Services for Professional Development10.1007/978-3-642-00978-5_8
Article
Ecology of social search for learning resourcesCampus-Wide Information Systems10.1108/10650740910984619
Chapter
Panorama of Recommender Systems to Support LearningRecommender Systems Handbook10.1007/978-1-4899-7637-6_12
Article
Semantic Learning Service PersonalizedInternational Journal of Computational Intelligence Systems10.1080/18756891.2012.670528
Chapter
Recommender Systems in Technology Enhanced LearningRecommender Systems Handbook10.1007/978-0-387-85820-3_12
Chapter
Providing Recommendations for Mobile LearningEducational Recommender Systems and Technologies10.4018/978-1-61350-489-5.ch013
Chapter
How the Crowd Can TeachHandbook of Research on Social Software and Developing Community Ontologies10.4018/978-1-60566-208-4.ch001
Article
An e-Learning Collaborative Filtering Approach to Suggest Problems to Solve in Programming Online JudgesInternational Journal of Distance Education Technologies10.4018/ijdet.2014040103

Social Recommender Systems: Recommendations in Support of E-Learning

Sheizaf Rafaeli, Yuval Dan-Gur, Miri Barak

Source Title: International Journal of Distance Education Technologies (IJDET)3(2)

Cite Article Cite Article

MLA

Rafaeli, Sheizaf, et al. "Social Recommender Systems: Recommendations in Support of E-Learning." IJDET vol.3, no.2 2005: pp.30-47. http://doi.org/10.4018/jdet.2005040103

APA

Rafaeli, S., Dan-Gur, Y., & Barak, M. (2005). Social Recommender Systems: Recommendations in Support of E-Learning. International Journal of Distance Education Technologies (IJDET), 3(2), 30-47. http://doi.org/10.4018/jdet.2005040103

Chicago

Rafaeli, Sheizaf, Yuval Dan-Gur, and Miri Barak. "Social Recommender Systems: Recommendations in Support of E-Learning," International Journal of Distance Education Technologies (IJDET) 3, no.2: 30-47. http://doi.org/10.4018/jdet.2005040103

Export Reference

Favorite Full-Issue Download

View Full Text PDF

Abstract

Recommendation systems can play an extensive role in online learning. In such systems, learners can receive guidance in locating and ranking references, knowledge bits, test items, and so forth. In recommender systems, users’ ratings can be applied toward items, users, other users’ ratings, and, if allowed, raters of raters of items recursively. In this chapter, we describe an online learning system — QSIA — an active recommender system for Questions Sharing and Interactive Assignments, designed to enhance knowledge sharing among learners. First, we lay out some of the theoretical background for social, open-rating mechanisms in online learning systems. We discuss concepts such as social versus black-box recommendations and the advice of neighbors as opposed to that of friends. We argue that enabling subjective views and ratings of other users is an inevitable phase of social collaboration systems. We also argue that social recommendations are critical for the exploitation of the value associated with recommendation.

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

Social Recommender Systems: Recommendations in Support of E-Learning

MLA

APA

Chicago

Export Reference

Abstract

Request Access