Tuning Journal for Higher Education (Nov 2023)
The effects of online learning self-efficacy and attitude toward online learning in predicting academic performance: The case of online prospective mathematics teachers
Abstract
This study aims to discover if Online Learning Self-Efficacy (OLSE) and attitude toward online learning (AOL) significantly predict the academic performance (AP) among Turkish prospective mathematics teachers. Unlike the studies conducted in the literature, online learning self-efficacy and attitude towards online learning as predictor variables were included in the study and both quantitative and qualitative data were collected. The study included 1075 prospective mathematics teachers’ responses in the analysis. The Pearson correlation was employed to determine how strongly OLSE, AOL, and AP are related. Results indicated that OLSE and AOL influenced the level of AP. Also, the multiple regression aimed to predict AP based on OLSE and AOL, and this model explained 44.6% of the variance in AP. The beta weights demonstrated that OLSE and AOL (OLSE β = .36, t(1072) = 9.705, p < .001, and AOL β = .34, t(1072) = 9.176, p < .001) significantly contributed to the model. The results showed that the level of academic performance can be predicted by online learning self-efficacy and attitude toward online learning. In addition, this study revealed the factors that have favorable and adverse effects on the academic performance of prospective mathematics teachers to gain more extensive information. Under the theme of negative factors, there were 7 codes. The results obtained from the study can be a guide for practitioners, policy makers and teachers to take the necessary precautions for the effective execution of the distance education process. Received: 4 October 2021 Accepted: 27 June 2023
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