通过TF-IDF和Word2vec文本分析调查响应行为:PISA 2012问题解决过程数据的案例研究.
Jing Zhou1, Zhanliang Ye1, Sheng Zhang1
1Collaborative Innovation Center of Assessment Towards Basic Education Quality, Beijing Normal University, No. 19, XinJieKouWai St., HaiDian District, Beijing, 100875, PR China Beijing, China.
Heliyon
|September 9, 2024
概括
像TF-IDF和Word2vec这样的文本分析方法有效地从基于计算机的解决问题数据中提取特征. 这些方法增强机器学习模型,以获得更好的评估见解.
科学领域:
- 教育的测量和评估.
- 计算语言学计算语言学
- 机器学习在教育中的应用
背景情况:
- 基于计算机解决问题的 (CBPS) 评估中的过程数据包含隐性信息.
- 不规则的数据结构阻碍了有效的特征提取,导致信息丢失.
- 过程响应行为与文本数据有相似之处,这表明文本分析的适用性.
研究的目的:
- 调查术语频率-反向文档频率 (TF-IDF) 和Word to Vector (Word2vec) 以提取响应行为特征.
- 使用这些提取的特征,比较监督和无监督的机器学习方法.
- 评估这些方法的预测,分析和集群性能.
主要方法:
- 应用了TF-IDF和Word2vec来从PISA 2012CBPS数据集中提取特征.
- 使用了经典的机器学习算法:随机森林,支持向量机 (SVM),物流回归,k-最近邻居 (k-NN).
- 采用k-means用于不受监督的响应行为模式的集群.
主要成果:
- TF-IDF有效地提取了关键响应行为;Word2vec从序列行为中捕获了特征.
- 在监督学习中,基于TF-IDF的Random Forest表现最好;基于Word2vec的SVM排名第二.
- Word2vec模型在F1得分,准确性和跨算法回忆方面普遍优于TF-IDF模型.
- K-means集群有效地识别了不同的响应行为模式.
结论:
- 文本分析方法 (TF-IDF,Word2vec) 可以转移到教育和心理评估中.
- 这些方法为细粒度评估提供了丰富的特征表示.
- 结果支持个性化学习,并为教育评估研究和实践提供新的见解.
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