Explainable eBook recommendation for extensive reading in K-12 EFL learning

Abstract

An automatic recommendation system for learning materials in e-learning addresses the challenge of selecting appropriate materials amid information overload and varying self-directed learning (SDL) skills. Such systems can enhance learning by providing personalized recommendations. In Extensive Reading (ER) for English as a Foreign Language (EFL), recommending materials is crucial due to the paradox that learners with low SDL skills struggle to select suitable ER resources, despite ER’s potential to improve SDL. Additionally, determining the difficulty level of ER materials and assessing learners’ progress remains challenging. The system must also explain its recommendations to foster motivation and trust. This study proposes a mechanism to estimate the difficulty of ER materials, adapted to learner preferences, using information retrieval techniques, and an explainable recommendation system for English materials. An experiment was conducted with 240 Japanese junior high school students in an ER program to assess the accuracy of difficulty estimation and identify learner characteristics receptive to the recommendations. While the recommendations did not significantly impact learners’ English skills or motivation, they were positively received. A strong relationship was found between the use and acceptance of recommendations and learners’ motivation. The study suggests that although the system did not increase overall motivation, it has potential to further enhance the motivation of naturally motivated learners.