Deep Reinforcement Learning for Engagement-Aware Question Selection in Adaptive Assessment Systems
Poster papers and late breaking results, workshops and tutorials, practitioners, industry and policy track, doctoral consortium, blue sky and wideAIED papers presented at the 26th International Conference on Artificial Intelligence in Education, AIED 2025, Palermo, İtalya, 22 - 26 Temmuz 2025, cilt.2592 CCIS, ss.27-34, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 2592 CCIS
- Doi Numarası: 10.1007/978-3-031-99267-4_4
- Basıldığı Şehir: Palermo
- Basıldığı Ülke: İtalya
- Sayfa Sayıları: ss.27-34
- Anahtar Kelimeler: adaptive assessment systems, learner engagement, personalized assessment, reinforcement learning
- Van Yüzüncü Yıl Üniversitesi Adresli: Evet
Özet
Unlike conventional assessments, adaptive assessment systems typically require substantially fewer questions to produce an accurate estimate of the learner’s ability. However, these systems work under the premise that the learner is fully engaged in the assessment, thereby putting their best effort into responding to the questions. Violations of this assumption can disrupt the question selection process, resulting in the underestimation of learners’ ability levels. To address this challenge, we propose a deep reinforcement learning approach that enhances adaptive question selection by considering both learner performance and engagement. Engagement is operationalized as a latent trait, inferred from response times. We implement a deep Q-network (DQN) strategy that selects questions based on the correctness of responses and engagement status. Using real data from a large-scale digital assessment, we optimize Q-networks to improve question selection outcomes. Also, we compare the accuracy of our approach with question selection in conventional adaptive assessments. The results show that by considering learner performance and engagement, DQN yields more accurate ability estimates, especially in shorter assessments. Also, the proposed method facilitates the learners to complete the assessment within the allotted time.