This research presents preliminary work to address the challenge of identifying at-risk students using supervised machine learning and three unique data categories: engagement, demographics, and performance data collected from Fall 2023 using Canvas and the California State University, Fullerton dashboard. We aim to tackle the persistent challenges of higher education retention and student dropout rates by screening for at-risk students and building a high-risk identification system. By focusing on previously overlooked behavioral factors alongside traditional metrics, this work aims to address educational gaps, enhance student outcomes, and significantly boost student success across disciplines at the University. Pre-processing steps take place to establish a target variable, anonymize student information, manage missing data, and identify the most significant features. Given the mixed data types in the datasets and the binary classification nature of this study, this work considers several machine learning models, including Support Vector Machines (SVM), Naive Bayes, K-nearest neighbors (KNN), Decision Trees, Logistic Regression, and Random Forest. These models predict at-risk students and identify critical periods of the semester when student performance is most vulnerable. We will use validation techniques such as train test split and k-fold cross-validation to ensure the reliability of the models. Our analysis indicates that all algorithms generate an acceptable outcome for at-risk student predictions, while Naive Bayes performs best overall.

翻译：本研究提出了一项初步工作，旨在利用监督式机器学习方法，结合三类独特的数据类别——参与度、人口统计学和学业表现数据（通过Canvas平台和加州州立大学富勒顿分校仪表板于2023年秋季采集），应对识别风险学生的挑战。我们致力于通过筛查风险学生并构建高风险识别系统，以应对高等教育留存率和学生辍学率这一长期难题。通过关注传统指标之外以往被忽视的行为因素，本研究旨在弥补教育评估的空白，改善学生学业表现，并显著提升大学跨学科学生的学业成就。研究实施了数据预处理步骤，包括建立目标变量、匿名化学生信息、处理缺失数据以及识别最显著的特征。考虑到数据集中混合的数据类型以及本研究的二分类性质，我们评估了多种机器学习模型，包括支持向量机（SVM）、朴素贝叶斯、K近邻（KNN）、决策树、逻辑回归和随机森林。这些模型用于预测风险学生，并识别学期中学生表现最脆弱的关键时段。我们将采用训练测试分割和K折交叉验证等验证技术以确保模型的可靠性。分析结果表明，所有算法在风险学生预测中均能产生可接受的结果，其中朴素贝叶斯模型整体表现最优。