Real-World Testing Matters in Reinforcement Learning for Education

Deep Reinforcement Learning (DRL) has proven its usefulness across various fields, sparking growing interest in applying it to education. However, most research on DRL in educational applications utilizes methods in simulation, with little evaluation involving real learners, resulting in limited evidence of their effectiveness in real-world contexts. Arguably, we consider real-world applications and in-situ experiments with users as essential for a thorough evaluation. We thus propose ResUli-RL, a novel DRL approach rooted in educational psychology, designed to provide adaptive feedback to young learners in the form of a pedagogical agent in a mobile educational app. To investigate its effectiveness, we conducted a five-week real-world evaluation with 56 primary school students, comparing ResUli-RL to an expert-designed baseline. Both groups significantly improved in reading competence, with no significant differences between them and a notable decrease in motivation in both conditions. In our aim to further improve the children's reading competence using DRL, our approach did, however, not yield the expected results. Our findings provide guidance for future work and highlight the need for real-world evaluations in education to assess the value of an educational DRL approach. CCS CONCEPTS • Applied computing → Interactive learning environments; • Human-centered computing → Field studies; • Computing methodologies → Reinforcement learning.