Educational Data Mining to Support Programming Learning Using Problem-Solving Data
Educational Data Mining to Support Programming Learning Using Problem-Solving Data
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DOI：
10.1109/access.2022.3157288
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Md. Mostafizer Rahman;Y. Watanobe;Taku Matsumoto;R. U. Kiran;Keita Nakamura
中科院分区：
文献类型：
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作者：
Md. Mostafizer Rahman;Y. Watanobe;Taku Matsumoto;R. U. Kiran;Keita Nakamura
计算机编程在现实世界的信息和通信技术的发展中引起了广泛的关注。满足 ICT 行业对高技能程序员不断增长的需求是主要挑战之一。在这一点上，在线法官（OJ）系统除了课堂学习之外还增强了编程学习和实践机会。因此，OJ 系统创建了大量的问题解决数据（解决方案代码、日志和分数）档案，这些档案可以成为编程教育研究的宝贵原材料。在本文中，我们提出了一种教育数据挖掘框架来支持使用无监督算法的编程学习。该框架包括以下步骤序列：(i) 解决问题的数据收集（从 OJ 收集日志和分数）和预处理； (ii) MK-means聚类算法用于欧氏空间中的数据聚类； (iii) 从每个簇中提取统计特征； (iv)对每个簇应用频繁模式（FP）增长算法来挖掘数据模式和关联规则； (v) 根据提取的特征、数据模式和规则提供一组建议。调整不同的参数以达到聚类和关联规则挖掘算法的最佳结果。该实验使用了来自 537 名编程课程（算法和数据结构）学生的大约 70,000 个现实世界问题解决数据。此外，合成数据已用于实验来证明 MK 均值算法的性能。实验结果表明，所提出的框架有效地从解决问题的数据中提取有用的特征、模式和规则。此外，这些提取的特征、模式和规则突出了编程学习中的弱点和可能改进的范围。
Computer programming has attracted a lot of attention in the development of information and communication technologies in the real world. Meeting the growing demand for highly skilled programmers in the ICT industry is one of the major challenges. In this point, online judge (OJ) systems enhance programming learning and practice opportunities in addition to classroom-based learning. Consequently, OJ systems have created a large number of problem-solving data (solution codes, logs, and scores) archives that can be valuable raw materials for programming education research. In this paper, we propose an educational data mining framework to support programming learning using unsupervised algorithms. The framework includes the following sequence of steps: (i) problem-solving data collection (logs and scores are collected from the OJ) and preprocessing; (ii) MK-means clustering algorithm is used for data clustering in Euclidean space; (iii) statistical features are extracted from each cluster; (iv) frequent pattern (FP)-growth algorithm is applied to each cluster to mine data patterns and association rules; (v) a set of suggestions are provided on the basis of the extracted features, data patterns, and rules. Different parameters are adjusted to achieve the best results for clustering and association rule mining algorithms. For the experiment, approximately 70,000 real-world problem-solving data from 537 students of a programming course (Algorithm and Data Structures) were used. In addition, synthetic data have leveraged for experiments to demonstrate the performance of MK-means algorithm. The experimental results show that the proposed framework effectively extracts useful features, patterns, and rules from problem-solving data. Moreover, these extracted features, patterns, and rules highlight the weaknesses and the scope of possible improvements in programming learning.