ENHANCING STUDENT ENGAGEMENT AND INTEREST IN LEARNING THROUGH RANDOM CLASSROOM ACTIVITIES: AN EXAMINATION OF FACTORS INFLUENCING STUDENT SATISFACTION USING RANDOM FOREST ALGORITHM
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After the COVID-19 pandemic, educational institutions shifted from online back to face-to-face learning. However, this transition resulted in decreased student engagement, mainly due to the prolonged period of prior online learning. Researchers sought to increase student engagement by implementing random classroom activities based on student interests. The purpose of this study was 1) to develop a conceptual framework for implementing random classroom activities based on students’ interests, 2) to evaluate students' satisfaction with the use of random classroom activities based on their interests, and 3) to identify factors that influence their satisfaction. Methods: The study used a comprehensive approach that included reviewing relevant theories, developing a conceptual framework, conducting experiments, collecting data, and analyzing data using descriptive statistics and the random forest algorithm. The study involved 60 undergraduate students (n = 60) and employed a mixed analytical approach, including descriptive statistics and a Random Forest algorithm, to explore non-linear relationships among learner-related variables. The Random Forest model achieved an overall classification accuracy of 83.33%, providing exploratory insights into factors associated with student satisfaction. Results: Students reported a high level of satisfaction with the random activities tailored to their interests, with a mean rating of 3.80 and a standard deviation of 1.08. Based on the random forest algorithm, the primary factor influencing student satisfaction was their grade point average (GPA), which had the highest importance value of 0.29. Conclusions: This result highlights the significant role that students’ academic performance and learning ability play in determining their level of satisfaction.
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