Quarantines imposed due to COVID-19 have forced the rapid implementation of e-learning, but also increased the rates of anxiety, depression, and fatigue, which relate to dramatically diminished e-learning motivation. Thus, it was deemed significant to identify e-learning motivating factors related to mental health. Furthermore, because computer programming skills are among the core competencies that professionals are expected to possess in the era of rapid technology development, it was also considered important to identify the factors relating to computer programming learning. Thus, this study applied the Learning Motivating Factors Questionnaire, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder Scale-7 (GAD-7), and the Multidimensional Fatigue Inventory-20 (MFI-20) instruments. The sample consisted of 444 e-learners, including 189 computer programming e-learners. The results revealed that higher scores of individual attitude and expectation, challenging goals, clear direction, social pressure, and competition significantly varied across depression categories. The scores of challenging goals, and social pressure and competition, significantly varied across anxiety categories. The scores of individual attitude and expectation, challenging goals, and social pressure and competition significantly varied across general fatigue categories. In the group of computer programming e-learners: challenging goals predicted decreased anxiety; clear direction and challenging goals predicted decreased depression; individual attitude and expectation predicted diminished general fatigue; and challenging goals and punishment predicted diminished mental fatigue. Challenging goals statistically significantly predicted lower mental fatigue, and mental fatigue statistically significantly predicted depression and anxiety in both sample groups.

Educational systems around the world encourage students to engage in programming activities, but programming learning is one of the most challenging learning tasks. Thus, it was significant to explore the factors related to programming learning. This study aimed to identify computer programming e-learners’ personality traits, self-reported cognitive abilities and learning motivating factors in comparison with other e-learners. We applied a learning motivating factors questionnaire, the Big Five Inventory—2, and the SRMCA instruments. The sample consisted of 444 e-learners, including 189 computer programming e-learners, the mean age was 25.19 years. It was found that computer programming e-learners demonstrated significantly lower scores of extraversion, and significantly lower scores of motivating factors of individual attitude and expectation, reward and recognition, and punishment. No significant differences were found in the scores of self-reported cognitive abilities between the groups. In the group of computer programming e-learners, extraversion was a significant predictor of individual attitude and expectation; conscientiousness and extraversion were significant predictors of challenging goals; extraversion and agreeableness were significant predictors of clear direction; open-mindedness was a significant predictor of a diminished motivating factor of punishment; negative emotionality was a significant predictor of social pressure and competition; comprehension-knowledge was a significant predictor of individual attitude and expectation; fluid reasoning and comprehension-knowledge were significant predictors of challenging goals; comprehension-knowledge was a significant predictor of clear direction; and visual processing was a significant predictor of social pressure and competition. The SEM analysis demonstrated that personality traits (namely, extraversion, conscientiousness, and reverted negative emotionality) statistically significantly predict learning motivating factors (namely, individual attitude and expectation, and clear direction), but the impact of self-reported cognitive abilities in the model was negligible in both groups of participants and non-participants of e-learning based computer programming courses; χ² (34) = 51.992, p = 0.025; CFI = 0.982; TLI = 0.970; NFI = 0.950; RMSEA = 0.051 [0.019–0.078]; SRMR = 0.038. However, as this study applied self-reported measures, we strongly suggest applying neurocognitive methods in future research.

Computer programming e-learners faced stressful life circumstances and educational changes that affected the world during the COVID-19 pandemic. As the cognitive model of flourishing focuses on cognitions rather than situations themselves, it was deemed significant to identify peer-to-peer confirmation, positive automatic thoughts, flourishing, and the links between these study variables in a group of computer programming e-learners and compare the results with other e-learners. This study applied the Flourishing Scale (FS), the Automatic Thoughts Questionnaire—Positive (ATQP), and the Student-to-Student Confirmation Scale. The sample consisted of 453 e-learners, including 211 computer programming e-learners. The results revealed that computer programming e-learners differed from other e-learners in flourishing, positive daily functioning, and peer-to-peer confirmation. In both samples, positive daily functioning and positive future expectations predicted self-reported flourishing. Positive automatic thoughts and flourishing predicted peer-to-peer confirmation just in the group of computer programming e-learners. The SEM analysis revealed that peer-to-peer confirmation and positive automatic thoughts explained 57.4% of the variance of flourishing in the computer programming e-learners group and 9.3% of the variance in the social sciences e-learners group, χ2 = 81.320, df = 36, p < 0.001; NFI = 0.963; TLI = 0.967; CFI = 0.979; RMSEA = 0.075 [0.053–0.096]; SRMR = 0.033. The findings signify the importance of peer-to-peer confirmation and positive thoughts for computer programming e-learners’ psychological well-being. Nevertheless, the results of this particular study should be regarded with caution due to the relatively small sample size and other limitations. In the future, it would be valuable to identify the underlying mechanisms and the added value of positive states such as flow, which have recently received the increased attention of researchers.