Creating a Predictive Model for Academic Performance in Statistics Courses for Undergraduate Students Using Data from Facebook API and Registration Records
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Abstract
The objective of this study was to construct a predictive model for undergraduate students’ academic performance in statistics courses by utilizing data obtained from the Facebook API and university registration records. A purposive sampling technique was employed to select a group of 543 undergraduate students enrolled in statistics courses. The dataset was compiled from students’ Facebook activity and their registration records. Data analysis was conducted using factor analysis and multiple regression via JAMOVI version 2.3.21. The findings revealed that both Facebook usage behavior and general information from registration recordssignificantly influenced students' academic performance in statistics courses, with statistical significance at the .05 level. The predictive model was as follows: SCORE = -35.392 - 1.132(PC1) - 4.403(PC2) + 26.621(CGPA) + 9.866(Semester: 1-2) + 13.598(Year: 2017-2019) + 14.651(Year: 2018-2019), with a determination coefficient of 55.10%. The research demonstrated the potential to enhance the model’s predictive capacity through two factors: 1) increasing the sample size, and 2) incorporating general information from registration records into the constructed model. Moreover, the integration of Facebook API data with general registration records, along with the predictive model, proved beneficial for predicting students’ academic performance – particularly in cases where special consultation may be required to provide timely academic support or to inform decisions for future improvement of learning outcomes.
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