人工标签和机器学习在聚类和预测学生成绩中的应用

Q2 Social Sciences
Mengjiao Yin, Hengshan Cao, Zuhong Yu, Xianyu Pan
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引用次数: 0

摘要

本研究提出了 "学业投资模型"(AIM),将学习风格作为一种预测特征,作为预测学生学业成绩的新方法。研究利用来自中国 138 名市场营销专业学生的数据,通过四象限聚类技术,将机器学习聚类方法和人工特征工程相结合。AIM 模型根据学生在学业上投入的时间和精力,将学生的投资划分为四个象限,区分结果导向型投资和过程导向型投资。研究结果表明,四象限法在预测准确性上超过了机器学习聚类,凸显了人工特征工程的稳健性。这项研究的意义在于,它可以指导教育工作者设计有针对性的干预措施和个性化学习策略,强调了以过程为导向的评估在教育中的重要性。建议未来的研究扩大样本量,并探索整合深度学习模型进行验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Manual Label and Machine Learning in Clustering and Predicting Student Performance
This study presents the Academic Investment Model (AIM) as a novel approach to predicting student academic performance by incorporating learning styles as a predictive feature. Utilizing data from 138 Marketing students across China, the research employs a combination of machine learning clustering methods and manual feature engineering through a four-quadrant clustering technique. The AIM model delineates student investment into four quadrants based on their time and energy commitment to academic pursuits, distinguishing between result-oriented and process-oriented investments. The findings reveal that the four-quadrant method surpasses machine learning clustering in predictive accuracy, highlighting the robustness of manual feature engineering. The study's significance lies in its potential to guide educators in designing targeted interventions and personalized learning strategies, emphasizing the importance of process-oriented assessment in education. Future research is recommended to expand the sample size and explore the integration of deep learning models for validation.
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来源期刊
CiteScore
2.40
自引率
0.00%
发文量
68
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