RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

Autor/inn/en	Khosravi, Hassan; Kitto, Kirsty; Cooper, Kendra
Titel	RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests
Quelle	In: Journal of Educational Data Mining, 9 (2017) 1, S.42-67 (26 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Teaching Methods; College Students; Educational Technology; Technology Uses in Education; Student Interests; Individualized Instruction; Multiple Choice Tests; Questioning Techniques; Peer Influence; Matrices; Web Sites; Test Construction; Programming; Foreign Countries; Prediction; Canada + Suchen Sie Ihr Suchwort? Teaching method; Lehrmethode; Unterrichtsmethode; Collegestudent; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Studieninteresse; Individualisierender Unterricht; Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren; Befragungstechnik; Fragetechnik; Matrizenrechnung; Web-Design; Testaufbau; Programmierung; Ausland; Vorhersage; Kanada
Abstract	Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior. (As Provided).
Anmerkungen	International Working Group on Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://www.educationaldatamining.org/JEDM/index.php/JEDM/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01