Data Mining of Formative and Summative Assessments for Improving Teaching Materials towards Adaptive Learning: A Case Study of Programming Courses at the University Level

Abstract

It is crucial to review and update course materials regularly in higher education. However, in the course evaluation process, it is debatable what a difficult learning topic is. This paper proposes a data mining approach to detect learning topics requiring attention in the improvement process of teaching materials by analyzing the discrepancy between formative and summative assessments. In addition, we propose specific methods involving clustering and noise reduction using the OPTICS algorithm and discrepancy calculation steps. Intensive experiments have been conducted on a dataset collected from accurate assessment results of the data structures and algorithms (DSA) course for IT major students at our university. The experimental results have shown that noise reduction can assist in identifying underperforming and overperforming students. In addition, our proposed method can detect learning topics with a high discrepancy for continuously improving teaching materials, which is essential for question recommendation in adaptive learning systems.

Semi-Supervised Feature Selection of Educational Data Mining for Student Performance Analysis

“…With the development of the education field and the advancement of technology, EDM is rapidly emerging and has become a much-anticipated research direction in the field of education [24,25]. This section reviews the existing literature and research relevant to this paper's topic, aiming to achieve a comprehensive understanding of the current state of research on the impact of student characteristics on academic performance.…”