Predicting student’s successful completion of academic programs and the features that influence their performance can have a significant effect on improving students’ completion, and graduation rates and reduce attrition rates. Therefore, identifying students are at risk, and the courses where improvements in content, delivery mode, pedagogy, and assessment activities can improve students’ learning experience and completion rates. In this work, we have developed a prediction and explanatory model using adaptive neuro-fuzzy inference system (ANFIS) methodology to predict the grade point average (GPA), at graduation time, of students enrolled in the information technology program at Ajman University. The approach adopted uses students’ grades in introductory and fundamental IT courses and high school grade point average (HSGPA) as predictors. Sensitivity analysis was performed on the model to quantify the relative significance of each predictor in explaining variations in graduation GPA. Our findings indicate HSGPA is the most influential factor in predicting graduation GPA, with data structures, operating systems, and software engineering coming closely in second place. On the explanatory side, we have found that discrete mathematics was the most influential course causing variations in graduation GPA, followed by software engineering, information security, and HSGPA. When we ran the model on the testing data, 77% of the predicted values fell within one root mean square error (0.29) of the actual GPA, which has a maximum of four. We have also shown that the ANFIS approach has better predictive accuracy than commonly used techniques such as multilinear regression. We recommend that IT programs at other institutions conduct comparable studies and shed some light on our findings.

预测学生成功完成学术课程以及影响其表现的特征可以对提高学生的完成率和毕业率以及降低流失率产生重大影响。因此，识别有风险的学生，以及在内容、交付方式、教学法和评估活动方面改进的课程可以提高学生的学习体验和完成率。在这项工作中，我们开发了一个预测和解释模型，使用自适应神经模糊推理系统 (ANFIS) 方法来预测在阿治曼大学注册信息技术课程的学生在毕业时的平均绩点 (GPA)。采用的方法使用学生在 IT 入门和基础课程中的成绩以及高中平均绩点 (HSGPA) 作为预测指标。对模型进行了敏感性分析，以量化每个预测变量在解释毕业 GPA 变化方面的相对重要性。我们的研究结果表明，HSGPA 是预测毕业 GPA 的最有影响力的因素，数据结构、操作系统和软件工程紧随其后。在解释方面，我们发现离散数学是导致毕业 GPA 变化的最有影响的课程，其次是软件工程、信息安全和 HSGPA。当我们在测试数据上运行模型时，77% 的预测值落在实际 GPA 的一个均方根误差 (0.29) 之内，最大为 4。我们还表明，ANFIS 方法比多线性回归等常用技术具有更好的预测准确性。