Personalized and Automated Feedback in Summative Assessment Using Recommender Systems

Abstract

In this study we explore the use of recommender systems as a means of providing automated and personalized feedback to students following summative assessment. The intended feedback is a personalized set of test questions (items) for each student that they could benefit from practicing with. Recommended items can be beneficial for students as they can support their learning process by targeting specific gaps in their knowledge, especially when there is little time to get feedback from instructors. The items are recommended using several commonly used recommender system algorithms, and are based on the students scores in a summative assessment. The results show that in the context of the Dutch secondary education final examinations, item recommendations can be made to students with an acceptable level of model performance. Furthermore, it does not take a computationally complex model to do so: a simple baseline model which takes into account global, student-specific, and item-specific averages obtained similar performance to more complex models. Overall, we conclude that recommender systems are a promising tool for helping students in their learning process by combining multiple data sources and new methodologies, without putting additional strain on their instructors.