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

Abstract

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.

Cyber Risk Recommendation System for Digital Education Management Platforms

“…Without compromising the generality, we assume that the values of these QoEs are sorted in a truly ascending order, that is, Q A < Q B < Q C , which indicates that the higher the recommendation identifier ( A , B , C ), the better the recommendation. A simple and effective metric that allows QoE verification for each user is the relative visit time ratio, which we define as [ 17 , 18 ] where the denominator is the total time that other users devote to their visit simultaneously as i . So we can easily observe that when the sum of the times of other users increases, then either the risk becomes higher, or the same number of users spend total time on the visit, which gives us an indication of the risk of misuse of the platform.…”

Recommendation Systems for Education: Systematic Review

“…The phases suggested by [16] were taken as reference in order to conduct the systematic review of literature (SRL), and these are shown in the following diagram (Figure 1). education, target users are students, teachers, and academic advisors, and the recommended elements are educational materials, learning objects, papers, universities, and information such as that about courses, student performance, and the field of study [11].…”

“…The total score will have a value between 0 and 16, classifying them as deficient (0-2), sufficient (3)(4)(5), good (6-10), very good (11)(12)(13), and excellent (14-16).…”

Learning analytics for enhancing the usability of serious games in formal education: A systematic literature review and research agenda

“…Moreover, we only focused on a specific topic that investigates the impact of exploiting SEGs in formal education as well as the role of LA in improving the usability of such environments. Hence, future research can explore other issues and research challenges not addressed by the studies included in this review like the design of interactive LA dashboards and recommender systems in education (Deschênes, 2020 ).…”