Recommender systems to support learners’ Agency in a Learning Context: a systematic review

International Journal of Educational Technology in Higher Education

Michelle Deschênes¹ 

[1] Université Laval, 2325 rue de l’Université, G1V 0A6, Quebec, QC, Canada;

关键词: Recommender systems;    Collaborative filtering;    Content-based filtering;    Systematic review;   

DOI  :  10.1186/s41239-020-00219-w

来源: Springer

【 摘 要 】

Recommender systems for technology-enhanced learning are examined in relation to learners’ agency, that is, their ability to define and pursue learning goals. These systems make it easier for learners to access resources, including peers with whom to learn and experts from whom to learn. In this systematic review of the literature, we apply an Evidence for Policy and Practice Information (EPPI) approach to examine the context in which recommenders are used, the manners in which they are evaluated and the results of those evaluations. We use three databases (two in education and one in applied computer science) and retained articles published therein between 2008 and 2018. Fifty-six articles meeting the requirements for inclusion are analyzed to identify their approach (content-based, collaborative filtering, hybrid, other) and the experiment settings (accuracy, user satisfaction or learning performance), as well as to examine the results and the manner in which they were presented. The results of the majority of the experiments were positive. Finally, given the results introduced in this systematic review, we identify future research questions.

【 授权许可】

CC BY