用AI识别学习风格与辍学风险，提升教学精准度。
Using Artificial Intelligence to Improve Classroom Learning Experience
- 基于六项指标用逻辑回归分析学习偏好
- 87.39%准确率预测学业退学风险
- 适合教育科技与智能辅导系统开发者
本文探讨人工智能技术在改善课堂学习体验中的应用，重点介绍IBM、Microsoft、Google及ChatGPT等公司的贡献，以及脑信号分析的潜力。通过机器学习算法实现对学生学习风格的识别和学业退学风险的预测。采用逻辑回归进行二分类，利用评估成绩、课程时长、学习偏好等六个预测变量，准确识别学习偏好。一项包含76,519名候选人的案例研究，使用35个预测变量评估退学风险，逻辑回归测试准确率达87.39%，优于随机梯度下降分类器的83.1%。
原文摘要 · Abstract (English)
This paper explores advancements in Artificial Intelligence technologies to enhance classroom learning, highlighting contributions from companies like IBM, Microsoft, Google, and ChatGPT, as well as the potential of brain signal analysis. The focus is on improving students learning experiences by using Machine Learning algorithms to : identify a student preferred learning style and predict academic dropout risk. A Logistic Regression algorithm is applied for binary classification using six predictor variables, such as assessment scores, lesson duration, and preferred learning style, to accurately identify learning preferences. A case study, with 76,519 candidates and 35 predictor variables, assesses academic dropout risk using Logistic Regression, achieving a test accuracy of 87.39%. In comparison, the Stochastic Gradient Descent classifier achieved an accuracy of 83.1% on the same dataset.
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