EW-KNN: evaluating information technology courses in high school with a non-parametric cognitive diagnosis method

ABSTRACT

With the continuous development of education, personalized learning has attracted great attention. How to evaluate students’ learning effects has become increasingly important. In information technology courses, the traditional academic evaluation focuses on the student’s learning outcomes, such as “scores” or “right/wrong,” which seldom reflects the development of students’ cognitive level and lacks effective diagnostic information. This article proposes a non-parametric multi-level scoring cognitive diagnosis method based on the KNN and the characteristics of information technology courses named the EW-KNN (E-weight K-Nearest Neighbor). Compared with the KNN, the EW-KNN improved two key points. One is that it takes the number of IRP (Ideal Response Pattern) as the K value to adapt to different types of tests. The other is that the nearest neighbor distance is introduced to solve the problem of misjudgment of the categories. The Monte Carlo simulation method is used to test its performance. The results indicate that the EW-KNN has a higher accuracy rate and is suitable for information technology courses. Furthermore, the method is applied in information technology course to make a cognitive diagnosis of 120 students of high school in Shanghai. Results demonstrate that the EW-KNN can accurately diagnose each student’s cognition levels and knowledge structure accurately.

KEYWORDS:

• EW-KNN
• Cognitive diagnosis
• Multi-level scoring
• Non-parametric diagnostic methods
• Information technology course

Disclosure statement

No potential conflict of interest was reported by the author(s).

Statement on open date

Data are available.

Ethics statement

The research received approval from Human Subjects Protection Committee of the university.

Additional information

Notes on contributors

Wanxue Zhang

Wanxue Zhang received the B.S. and M.S. degrees in educational technology, from East China Normal University (ECNU), Shanghai, China, in 2017 and 2020, respectively. She is currently a Computer Teacher with the school of WeiYu high school, Shanghai, China.

Lingling Meng

Lingling Meng received the Ph.D. degree in computer application from East China Normal University, Shanghai, China, in 2014. She is currently an Associate Professor in Faculty of Education, Department of Education Information Technology in East China Normal University. Her research interests include personalized learning, academic evaluation based on cognitive diagnosis, learning analytics, and knowledge engineering.

Bilan Liang

Bilan Liang is currently working toward M.S. degree in educational technology at East China Normal University, Shanghai, China. Her current research interests include the application of Information and Communication Technology in teaching and knowledge tracing.