An Educational Data Mining System For Predicting And Enhancing Tertiary Students’ Programming Skill

Abstract

Educational Data Mining (EDM) has become a promising research field for improving the quality of students and the education system. Although EDM dates back to several years, there is still lack of works for measuring and enhancing the computer programming skills of tertiary students. As such, we, in this paper, propose an EDM system for evaluating and improving tertiary students’ programming skills. The proposed EDM system comprises two key modules for (i) classification process and (ii) learning process,. The classification module predicts the current status of a student and the learning process module helps generate respective suggestions and feedback to enhance the student’s quality. In particular, for the classification module, we prepare a real dataset related to this task and evaluate the dataset to investigate six key Machine Learning (ML) algorithms, Support Vector Machine (SVM), decision tree, artificial neural network, Random Forest (RF), k-nearest neighbor and naive Bayes classifier, using accuracy-related performance measure metrics and goodness of the fit. The experimental results manifest that RF and SVM can predict the students more accurately than the other models. In addition, critical factors analysis is accomplished to identify the critical features toward achieving high classification accuracy. At last, we design an improvement mechanism in the learning process module that helps the students enhance their programming skills.

College English Smart Classroom Learning Model Utilizing Data Mining Technology

“…The structural model of English classroom teaching is a stable structural form of the process of classroom teaching activities, including the process structural style composed of elements, links, and steps of classroom teaching activities. The application of big data, learning analysis and other technologies has made important changes in the structure of English classroom teaching (Marjan et al, 2023). Traditional classroom teaching is centered on teachers and teaching materials; material is taught by teachers and accepted by students.…”

Classification and prediction‐based machine learning algorithms to predict students’ low and high programming performance

“…The results showed that this approach provides an effective way to provide and predict early grade assessments of underperforming students, thereby more effectively guiding the student. According to Marjan et al [19], an educational data mining (EDM) system was proposed to measure and develop higher education students’ computer programming skills. The proposed EDM system includes two key modules for (i) the classification process and (ii) the learning process.…”