Mining informatics’ courses logs to predict students’ stress
| Autor: | Đurđević Babić, Ivana |
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| Přispěvatelé: | INAYATULLAH, SOHAIL, DUBOVICKI, SNJEŽANA, BILIĆ, ANICA, Dubovicki, Snježana, Huljev, Antonija |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Popis: | The current pandemic situation has caused significant changes in education and the use of learning management systems (LMS) has increased with an obvious need for online communication and collaboration. Such a sudden change in the organization of teaching, has induced stress in education stakeholders. Since it is known that stress can negatively affect students’ performance, this paper investigates whether students with higher or lower stress levels in the class average can be effectively predicted by mining logs of the informatics' courses from the LMS. Also, the aim is to reveal which variables are important for this models’ accuracy. Log data were obtained from the Moodle LMS and the perceived stress level of 126 students was collected. The results show that the mean value of participants' perceived stress is 22.85 (SD =6.04) and that there is a weak negative statistically significant correlation between perceived stress and the page component of the LMS log files at a 5% significance level (r=-. 2). The best neural network model achieved an overall accuracy of 66.67%. It was more effective in detecting students with higher than average stress levels among participants (75%) than those with lower stress levels (60%). The file and system components had the greatest impact on the model's performance, as revealed by the sensitivity analysis. |
| Databáze: | OpenAIRE |
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