Calculus as a math course is an important subject students need to succeed in, to venture into STEM majors. The paper focuses on the early detection of at-risk students in a calculus course which can provide the proper intervention that might help them succeed. Calculus has high failure rates which corroborate with the data collected from our University that shows us that 40% of the 3266 students whose data were used failed in their calculus course. Some existing studies similar to our paper make use of open-scale data that are lower in data count and perform predictions on low-impact MOOC-based courses. Paper proposes, an automatic detection method of academically at-risk students by using Learning Management Systems (LMS) activity data along with the student information system (SIS) data from our University for the Math course. The proposed method will detect students at risk by employing machine learning to identify key features that contribute to the success of a student. The model developed has a predictive accuracy of 73.5 % on the online modality of the Math course and has 87.8 % accuracy on the face-2-face (F2F) modality of the same class. Transfer student, a binary feature attributed to the highest feature importance.

A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student dropout in these courses. An effective student dropout prediction model of MOOC courses can identify the factors responsible and provide insight on how to initiate interventions to increase student success in a MOOC. Different features and various approaches are available for the prediction of student dropout in MOOC courses. In this paper, the data derived from a self-paced math course, College Algebra and Problem Solving, offered on the MOOC platform Open edX partnering with Arizona State University (ASU) from 2016 to 2020 is considered. This paper presents a model to predict the dropout of students from a MOOC course given a set of features engineered from student daily learning progress. The Random Forest Model technique in Machine Learning (ML) is used in the prediction and is evaluated using validation metrics including accuracy, precision, recall, F1-score, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) curve. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5%, respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model.

Identifying students who are at-risk of failing a mathematics course at the earliest possible moment allows for support and scaffolding to be applied when it can have greatest impact. However, because risk of non-success can arise from a complex interaction of factors, early detection of struggling students is difficult. Machine learning is particularly suited to modeling this challenging interplay of variables. In this study, we measure how well machine learning models can identify at-risk students before an entry-level university calculus course begins. Five classification algorithms were applied to data combined from the student information system, an adaptive placement test, and a student survey. We were able to produce predictions before class start that were competitive with other studies using course activity data after coursework began. In addition, important features of the model provided insights into possible causes of student non-success.