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

Michelle Deschênes (2020)

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  • 2008
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  • Content-based
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  • Offline experiments (accuracy)
  • User studies (satisfaction)
  • Online experiments (learning performance)

Recommender systems for technology-Enhanced Learning are an area of research that can improve learners’ agency, that is, their ability to define and pursue learning goals. Recommender systems are a powerful method for making it easier for learners to access resources, including peers with whom to learn and experts from whom to learn.

Here are the results of a systematic review of the literature, in which an EPPI approach was applied. We examined the context in which recommenders are used, the manners in which they are evaluated and the results of those evaluations. We used 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 were 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.

Description of results



Supported tasks


Offline experiments (accuracy)

User studies (satisfaction)

Online experiments (learning performance)