Beyond binary correctness: Classification of students’ answers in learning systems

Adaptive learning systems collect data on student performance and use them to personalize system behavior. Most current personalization techniques focus on the correctness of answers. Although the correctness of answers is the most straightforward source of information about student state, research suggests that additional data are also useful, e.g., response times, hints usage, or specific values of incorrect answers. However, these sources of data are not easy to utilize and are often used in an ad hoc fashion. We propose to use answer classification as an interface between raw data about student performance and algorithms for adaptive behavior. Specifically, we propose a classification of student answers into six categories: three classes of correct answers and three classes of incorrect answers. The proposed classification is broadly applicable and makes the use of additional interaction data much more feasible. We support the proposal by analysis of extensive data from adaptive learning systems.