Contribution: This study applies supervised and unsupervised machine learning (ML) techniques to discover which significant attributes that a successful learner often demonstrated in a computer course. Background: Students often experienced difficulties in learning an introduction to computers course. This research attempts to investigate how successful students regulate their learnings in this course. The answer to these questions will provide teachers with useful information to better comprehend how students learn and which strategies are effective in learning. Research Questions: 1) Which algorithm in supervised learning is the best one for predicting students’ final performance? and 2) What attributes are key to succeed in this course? Methodology: Seven supervised ML algorithms and ensembles are conducted to compare the performance of classifiers regarding the levels of accuracy, precision, and sensitivity. The association rule and clustering are also employed to discover the key attributes for successful students. Because the present study used a convenience sample for data analysis, the number of students in each cluster was a potential limitation. Findings: The results show that Naïve Bayes is the most appropriate one for predicting students’ final performance. The measure in accuracy and sensitivity of this classifier achieves 83.26% and 92.88%, respectively. In addition, the association rule indicates that “to make sure keep up with the weekly progress in the class” and self-efficacy beliefs play important roles on final performances for learners. The clustering findings reveal similar results.

中文翻译：

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应用机器学习技术检查大学生的成功属性

贡献： 这项研究应用了有监督的和无监督的机器学习（ML）技术来发现成功的学习者经常在计算机课程中展示哪些重要属性。 背景：学生在学习计算机课程入门时经常遇到困难。这项研究试图调查成功的学生如何调节本课程的学习。这些问题的答案将为教师提供有用的信息，以更好地理解学生的学习方式以及有效学习的策略。研究问题：1）监督学习中哪种算法是预测学生最终成绩的最佳算法？2）什么属性是成功完成本课程的关键？方法：进行了七个监督的ML算法和集成，以比较分类器在准确性，精确度和灵敏度方面的性能。关联规则和聚类也被用来发现成功学生的关键属性。由于本研究使用便利样本进行数据分析，因此每个集群中的学生人数是一个潜在的限制。发现：结果表明，朴素贝叶斯是最适合预测学生最终成绩的一种。该分类器的准确度和敏感度分别达到83.26％和92.88％。另外，联想规则表明“确保跟上班上的每周进度”，自我效能感信念对学习者的最终成绩起着重要作用。聚类结果揭示了相似的结果。