Predicting Student Performance in an ITS Using Task-Driven Features

Abstract

Intelligent Tutoring Systems (ITS) are typically designed to offer one-on-one tutoring on a subject to students in an adaptive way so that students can learn the subject at their own pace. The ability to predict student performance enables an ITS to make informed decisions towards meeting the individual needs of students. It is also useful for ITS designers to validate if students are actually able to succeed in learning the subject. Predicting student performance is a function of two complex and dynamic factors: (f1) student learning behavior and (f2) their current knowledge in the subject. Learning behavior is captured from student interaction with the ITS (e.g. time spent on an assigned task) and is stored in the form of web logs. Student knowledge in the subject is represented by the marks they score in assigned tasks and is stored in a specific component of the ITS called student model. In order to build an accurate prediction model, this raw data from student model and web logs must be engineered carefully and transformed into meaningful features. Existing systems such as LON-CAPA predict students performance using their learning behavior alone, without considering their (current) knowledge on the subject. Lack of proper feature engineering is evident from the low values of accuracy of their prediction models. This research proposes a highly accurate model that predicts student success in assigned tasks with a 96% accuracy by using features that are better informed not only about students in terms of the two factors f1 and f2 mentioned above, but also on the assigned task itself (e.g. task's difficulty level). In order to accomplish this, an Example Recommendation System (ERS) is designed with a fine-grained student model (to represent student data) and a fine-grained domain model (to represent domain resources such as tasks).