An artificial intelligence approach to monitor student performance and devise preventive measures

Abstract

A major problem an instructor experiences is the systematic monitoring of students’ academic progress in a course. The moment the students, with unsatisfactory academic progress, are identified the instructor can take measures to offer additional support to the struggling students. The fact is that the modern-day educational institutes tend to collect enormous amount of data concerning their students from various sources, however, the institutes are craving novel procedures to utilize the data to magnify their prestige and improve the education quality. This research evaluates the effectiveness of machine learning algorithms to monitor students’ academic progress and informs the instructor about the students at the risk of ending up with unsatisfactory result in a course. In addition, the prediction model is transformed into a clear shape to make it easy for the instructor to prepare the necessary precautionary procedures. We developed a set of prediction models with distinct machine learning algorithms. Decision tree triumph over other models and thus is further transformed into easily explicable format. The final output of the research turns into a set of supportive measures to carefully monitor students’ performance from the very start of the course and a set of preventive measures to offer additional attention to the struggling students.

Revolutionizing education with AI: Exploring the transformative potential of ChatGPT

“…It increases their learning interest (Lin & Chang, 2020), facilitates interactive learning environments via particular tools like Smart Sparrow that promote learner engagement with educational content (Karsenti, 2019). Numerous studies have also demonstrated substantial improvements in academic performance through the aid of AI technologies (Khan et al, 2021;Kim et al, 2021). It significantly improves learning outcomes and student happiness (Winkler & Söllner, 2018), maximizing student learning abilities and achievement (Clarizia et al, 2018).…”

A Two-Stage Early Prediction Model to Monitor the Students' Academic Progress

“…Evaluation of the performance of the prediction model was carried out using a confusion matrix [7]. The performance measure was based on the confusion matrix that is usually used is accuracy.…”

“…The performance measure was based on the confusion matrix that is usually used is accuracy. Accuracy is the ratio between the number of correctly predicted results compared to the number of data [1], [7]. Calculation of accuracy can be done using (1).…”

“…There are two kinds of efforts to reduce the dropout rate and the percentage of students who graduate beyond the normal study period, namely screening prospective students so that students accepted by higher education are excellent students and increasing the passing rate for higher education students. To increase course passing rates, higher educations can make various efforts to help students, especially those with less ability, by providing academic advisors [6], guardian lecturers, assistance, and tutoring from lecturers outside of class hours [7]. In order that the efforts made by the higher education are on target and run optimally, it is necessary to have supporting information, such as academic data.…”

Tukey Regressive Hoover Indexed Deep Shift-Invariant Neural Network for Student Behavior Prediction

“…But it failed to consider more information on students' performance to possibly predict their performance using deep learning approaches. The effectiveness of machine learning algorithms was estimated in [6] A deep neural network (DNN) was introduced in [7] to enhance the predictive approaches for students' academic performance. But the performance of accuracy of prediction was not improved.…”