Improving the Teaching Assessment System for the AI Comprehensive Practice Course Based on the K-means Algorithm within the CBA Framework
Year: 2024
Type: conference-paper
Abstract: Traditional assessment systems can't fully evaluate students' practical abilities in the course teaching process. To address the issue, we develop a teaching evaluation system for AI course. It is based on the Competition-Based Assessment (CBA) framework. The weights of evaluation criteria are optimized dynamically by using K-Means clustering and Spearman's rank correlation analysis. So that the system's accuracy and fairness has been enhanced. In the proposed system, the students' competencies are better reflected in the aspect of practical ability, innovation, and teamwork spirit. The experiment results show that our evaluation system provides a more objective assessment approach. Moreover, it can reflect students' practical skills effectively.
Authors: Xibin Xiao, Hao Chu, Fei Wang, Shiguang Wen
Institution: Northeastern University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4409965487