Curriculum Evaluation System Based on Association Rules and Cluster Analysis
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摘要: 高校在长期的教学活动中积累了大量的课程数据，如何利用数据资源分析课程教学状况，为提高课程教学质量提供决策支持，具有重要的研究价值。本文设计实现了一个基于关联规则与聚类分析的课程评价体系，对课程评价系统进行了功能需求分析，并对课程评价数据进行预处理。采用FP-growth算法对学生课程成绩数据进行关联规则分析，采用K-means++算法进行聚类分析，提高了课程数据分析的精度，实现了课程评价的自动化，提高了效率和评价的客观性。
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关键词:
- 课程评价 /
- 数据预处理 /
- FP-growth算法 /
- 关联规则 /
- 数据聚类
Abstract: The quality of curriculum is the fundamental factor for the continuous improvement of the teaching quality and it is also the basis to realize the reform of higher education. In the long-term teaching activities, colleges and universities have accumulated a large number of curriculum data. How to use these resources to evaluate the teaching situation and provide decision support for improving the quality of course teaching is of great research value. This paper designs a curriculum evaluation system based on the association rules and cluster analysis, analyzes the functional requirements of the curriculum evaluation, and preprocesses the course evaluation data. The FP-growth algorithm is used to analyze the association rules of the score of student course and the K-means++ algorithm is used for cluster analysis. These can effectively improve the analysis accuracy of course data, realize the automation of course evaluation, and improve the efficiency and objectivity of evaluation.-
Key words:
- curriculum evaluation /
- data preprocessing /
- FP-growth algorithm /
- association rule /
- data cluster
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图 2 报表6A：指标点相关考核项分解说明基本样式
Figure 2. Report 6A: Basic styles of decomposition of relevant assessment items of indicator points
表 1 考试成绩离散化处理
Table 1. Discretization of examination score
Discretization judgment Grade Representation Examination result>Average score Excellent 1 Examination result≤Average score Not excellent 0 
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表 2 课程关联规则
Table 2. Curriculum association rules
Antecedent Consequent Confidence level/% Linear algebra，
theory of probability，
digital logicDiscrete
mathematics90.00 Assembly language，
microcomputer principlesOperating system 96.29 College english，
introduction to computerComputer
professional english88.76 Assembly language，
composition principle，
computer architectureMicrocomputer
principles97.23 $ \vdots $ $\vdots $ $\vdots $
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表 3 第2个聚类中心的计算
Table 3. Calculation of the second clustering center
Sample number D(x)2 P(x) S 1 8 0.200 0.200 2 13 0.325 0.525 3 5 0.125 0.650 4 10 0.250 0.900 5 1 0.025 0.925 6 0 0 0.925 7 2 0.050 0.975 8 1 0.025 1
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表 4 离散数学课程最终聚类中心
Table 4. Final clustering center ofdiscrete mathematics
Type Standardized test scores Standardized regular scores The first 0.9862 0.8824 The second 0.9598 0.7149 The third 0.9422 0.4379 The fourth 0.6000 0.5975
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