Predicting Course Grades Through Comprehensive Modeling of Students’ Learning Behavioral Patterns

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Complexity Pub Date : 2025-02-07 DOI:10.1155/cplx/8851264
Danial Hooshyar, Yeongwook Yang
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Abstract

While modeling students’ learning behavior or preferences has been found to be a crucial indicator for their course achievement, very few studies have considered it in predicting the achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students’ learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students’ feature vectors and behavior models constitute a comprehensive student’s learning behavioral pattern which is later used for the prediction of their course achievement. Lastly, using a multiple-criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students’ achievement in courses with different numbers of students and features, showing the stability of the approach. Decision tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students’ course achievement with high accuracy through modeling their learning behavior during online courses.

Abstract Image

通过学生学习行为模式的综合建模预测课程成绩
虽然已经发现学生的学习行为或偏好建模是学生课程成绩的一个重要指标,但很少有研究在预测学生的在线课程成绩时考虑到这一点。本研究旨在对学生在线学习行为进行建模,并据此预测学生的课程成绩。首先,在课程中使用聚合的动作日志来开发特征向量。其次,将其中一些特征向量量化为三个数值,用于模拟学生的学习行为,即访问学习资源(content access)、与同伴互动(engagement)和参加评估测试(assessment)。学生的特征向量和行为模型构成了一个全面的学生学习行为模式,并用于预测学生的课程成绩。最后,采用多准则决策方法(即TOPSIS),确定了不同规模课程的最佳分类方法。我们的研究结果表明,所提出的概化方法可以成功地预测学生在不同人数和不同特征的课程中的成绩,显示了该方法的稳定性。决策树和AdaBoost分类方法在不同的数据集上表现优于其他现有方法。此外,我们的研究结果证明,通过建模学生在网络课程中的学习行为来准确预测学生的课程成绩是可行的。
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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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