The timing of degree completion for students taking post-secondary courses has been a constant source of angst for administrators wanting the best outcomes for their students. Most methods for predicting student degree completion extensions are completed by analog methods using human effort to analyze data. The majority of data analysis reporting of degree completion extension variables and impacts has, for decades, been done manually. Administrators primarily forecast the factors based on their expertise and intuition to evaluate implications and repercussions. The variables are large, varied, and situational to each individual and complex. We used machine learning (automated processes using predictive algorithms) to predict undergraduate extensions for at least 2 years beyond a standard 4 years to complete a bachelor's degree. The study builds a machine learning-based education understanding XAI model (ED-XAI) to examine students’ dependent and independent variables and accurately predict/explain degree extension. The study utilized Random Forest, Support Vector Machines, and Deep Learning Machine learning algorithms. XAI used Information Fusion, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME) models to explain the findings of the Machine Learning models. The ED-XAI model explained multiple scenarios and discovered variables influencing students’ degree completion linked to their status and funding source. The Random Forest model gave supreme predictive results with 89.1% Mean ROC, 71.6% Overall Precision, 86% Overall Recall, and 71.6% In-class Precision. The educational information system introduced in this study has significant implications for accurate variables reporting and impacts on degree extensions leading to successful degree completions minimally reported in higher education marketing analytics research.

参加高等教育课程的学生完成学位的时间一直是管理人员担心学生获得最佳结果的一个问题。大多数预测学生学位完成延期的方法都是通过人工分析数据的模拟方法来完成的。几十年来，大多数关于学位完成扩展变量和影响的数据分析报告都是手动完成的。管理员主要根据他们的专业知识和直觉来预测因素，以评估影响和影响。变量很大、多种多样，并且因每个人的情况而异且复杂。我们使用机器学习（使用预测算法的自动化流程）来预测本科生延期至少比标准 4 年延长 2 年才能完成学士学位。该研究构建了一个基于机器学习的教育理解 XAI 模型（ED-XAI）来检查学生的因变量和自变量，并准确预测/解释学位扩展。该研究利用了随机森林、支持向量机和深度学习机器学习算法。XAI 使用信息融合、SHApley 加法解释 (SHAP) 和局部可解释模型不可知解释 (LIME) 模型来解释机器学习模型的发现。ED-XAI 模型解释了多种场景，并发现了影响学生学位完成的变量，这些变量与其状态和资金来源相关。随机森林模型给出了最高的预测结果，平均 ROC 为 89.1%，总体精度为 71.6%，总体召回率为 86%，同级精度为 71.6%。本研究中引入的教育信息系统对于准确的变量报告以及对学位扩展的影响具有重大影响，从而导致高等教育营销分析研究中很少报告的成功完成学位。