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Analysis of the Relationship Between Computer Laboratory Usage Behavior and Academic Achievement Among Undergraduate Students |
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| รหัสดีโอไอ | |
| Creator | Pavinee Inthong |
| Title | Analysis of the Relationship Between Computer Laboratory Usage Behavior and Academic Achievement Among Undergraduate Students |
| Contributor | Seksan Sivilai, Pawat Chimlek, Sukanya Samutkhet, Thongrob Auxsorn |
| Publisher | Pibulsongkram Rajabhat University |
| Publication Year | 2569 |
| Journal Title | Humanities and Social Sciences Journal of Pibulsongkram Rajabhat University |
| Journal Vol. | 20 |
| Journal No. | 1 |
| Page no. | 141-158 |
| Keyword | Computer laboratory usage behavior, Academic achievement, Learning analytics, At-risk students, Higher education, Logistic regression |
| URL Website | https://so01.tci-thaijo.org/index.php/GraduatePSRU |
| Website title | Humanities and Social Sciences Journal of Pibulsongkram Rajabhat University |
| ISSN | 2985-0487 (Online) |
| Abstract | This study aimed to: 1) examine students’ computer laboratory usage behavior, 2) analyze the relationship between laboratory usage behavior and academic achievement, and 3) propose guidelines for monitoring and supporting at-risk students based on the concept of Learning Analytics. This quantitative research employed secondary data obtained from the computer laboratory usage log system and students’ academic records from the second semester of academic year 2024 and the first semester of academic year 2025. The sample consisted of 105 students selected through purposive sampling based on the completeness of data from both sources. The data were prepared, validated, transformed into individual-level variables, and analyzed using descriptive statistics, Pearson’s correlation coefficient, and Logistic Regression.The findings revealed that students demonstrated a moderate to high level of computer laboratory usage behavior. Laboratory usage behavior was related to academic achievement, particularly the number of usage days, number of visits, and total usage duration. In addition, the Logistic Regression model was able to support the classification of at-risk students at an acceptable level, with an AUC of 0.722, accuracy of 81.30%, precision of 79.60%, recall of 77.80%, and F1-score of 78.70%. The results indicate that log data from the computer laboratory usage system can serve as a useful database for developing a monitoring and early warning system for at-risk students, thereby supporting proactive student assistance in higher education effectively. |
