Process Evaluation for Diversified Academic Assessment Mechanism in Higher Education Institutions by Use of Data Mining

Abstract

A diversified academic assessment mechanism can effectively improve students’ learning motivation, make up for the possible blind spots of a single assessment method, and better guide students’ learning and teachers’ teaching. Using data mining methods to process evaluation data for diversified academic assessment mechanisms in colleges and universities can discover patterns in students’ learning, find key factors affecting academic performance, and provide a basis for teaching reform. Most of the current process evaluation data mining methods focus on hard skills, such as academic performance and classroom participation, but it is difficult to evaluate soft skills such as critical thinking and teamwork. To this end, this paper studies the process evaluation data mining methods for a diversified academic assessment mechanism in colleges and universities. It constructs an indicator system for process evaluation of diversified academic assessment mechanism in colleges and universities, gives a quantitative method for indicators, and performs fuzzy comprehensive evaluation based on AHP-entropy weight method. For the evaluation of text-based indicators, a consistency training method is introduced to train the process evaluation correlation mining model using a large amount of unlabeled process evaluation examples, which effectively solves the problems of lack of labeled data, high labeling cost, and changes in data distribution, and improves the performance and availability of the model. The experimental results verify the effectiveness of the proposed method.