Rimašiūtė-Knabikienė, Romualda

Introduction – E-learning is a central component of higher education, yet maintaining student motivation remains difficult. Although personality traits, mental health, and peer support have each been linked to learning motivation, they are seldom examined simultaneously within an integrative framework. Drawing on Self-Determination Theory, this study examined the associations among personality traits, anxiety and depression, peer-to-peer confirmation, and learning motivating factors in e-learning. Methods – A cross-sectional online survey was completed by 595 e-learners from Lithuanian universities and Turing College. Data were collected using an online self-report questionnaire. Participants completed the Learning Motivating Factors Questionnaire, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder Scale (GAD-7), Student-to-Student Confirmation Scale, and Big Five Inventory-2 (BFI-2). Pearson correlational analyses and structural equation modeling (SEM) were used to estimate associations and indirect pathways. Results – Extraversion, agreeableness, conscientiousness, and openness to experience showed positive correlations with e-learning motivation (r = 0.08–0.39, p < 0.01), whereas neuroticism and mental health symptoms were negatively correlated. The integrated SEM indicated acceptable fit: χ2 (39) = 221, p < 0.001; CFI = 0.921; TLI = 0.888; RMSEA = 0.089 (90% CI [0.077, 0.100]), RMSEA p < 0.001, and SRMR = 0.075. The association between personality traits and e-learning motivation was primarily indirect via peer-to-peer confirmation (β = 0.062), whereas the indirect pathway via mental health was not significant. Peer-to-peer confirmation was positively associated with e-learning motivation (β = 0.256, p < 0.001). Conclusion – Within the proposed model, peer-to-peer confirmation showed the strongest association with e-learning motivation, indicating the relevance of supportive peer interaction for e-learning motivation.

Introduction: Previous research employing latent profile analysis has considerably advanced our understanding of student motivation in online learning environments. However, a gap remains in exploring how mental health and social dimensions—specifically anxiety, depression, and peer-to-peer confirmation—influence these motivational profiles. Although prior studies indicate associations between student learning motivation, mental health, and peer-to-peer confirmation, their role in motivation profile is less understood. The current study aims to explore motivational profiles of e-learners, their differences in mental health, and their links to peer-to-peer confirmation. Methods: A cross-sectional survey of 595 university e-learners (33.3% male, 66.7% female; mean age 26.34 years, SD 8.4; age range 18–56) was conducted. Four instruments were used in this study: the Learning Motivating Factors Questionnaire, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder Scale (GAD-7), and Student-to-Student Confirmation Scale. Latent profile analysis (LPA) identified motivation profiles. Binomial logistic regression tested whether peer-to-peer confirmation dimensions predicted profile membership, and independent-samples t-tests compared anxiety and depression between profiles. Results: The latent profile analysis (LPA) identified high motivation profile and low motivation profile. The results of the binomial logistic regression revealed that peer-to-peer confirmation, namely, individual attention, was a significant predictor of student motivation: higher individual attention predicted high motivation profile membership, suggesting that personalized interactions between peers serve as a protective factor against low motivation. Additionally, e-learners in the low motivation profile had significantly higher levels of anxiety and depression. Conclusion: This study contributes to the growing research on student motivation, peer-to-peer confirmation, and mental health in e-learning. The latent profile analysis underscored the importance of individual attention as a unique and powerful factor in motivating students in e-learning environments. As higher education continues to embrace e-learning models, it will be essential to integrate effective mechanisms for peer interactions and communication processes to enhance student motivation. Additionally, the findings revealed that e-learners in the low motivation profile had significantly higher levels of anxiety and depression, which suggests that students' mental health should be among the priorities of education policies targeting correlates of academic achievements. Future studies should examine factors that are both protective for e-learners mental health and beneficial for learning outcomes.

Previous studies reported that acquiring computer programming skills is challenging and might result in high dropout rates. A quasi-experimental design was used to examine the role of different factors in dropping out of an e-based computer programming course. This study applied a knowledge in programming assessment test (20 multiple-choice questions covering the following topics: variables, loops, conditionals, functions, and general knowledge of Python), The Learning Motivating Factors Questionnaire, The Big Five-2, and The Basic Psychological Need Satisfaction & Frustration Scale. Ninety-four participants (38 males and 56 females) completed the course, while 305 participants started it. The mean age of e-learners was 29.96 years (SD 8.27), age range = 18 to 54. The results showed that e-learners who completed the course had higher initial knowledge assessment scores than those who dropped out after the first assessment. Reward and recognition as a motivator were significantly higher in males who completed the course than those who dropped out after the second knowledge assessment. Extraversion was significantly lower in females who completed the course than those who dropped after the first or second knowledge assessment test. Relatedness frustration was significantly higher in those who dropped out after the first knowledge assessment. Due to significant limitations of the sample size, cultural context, measures applied, and research design, the findings would preferably be regarded with caution.

Quarantines imposed due to COVID‐19 have forced the rapid implementation of e‐learn‐ ing, 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 devel‐ opment, 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 Mul‐ tidimensional 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 individ‐ ual attitude and expectation, challenging goals, clear direction, social pressure, and competition significantly varied across depression categories. The scores of challenging goals, and social pres‐ sure and competition, significantly varied across anxiety categories. The scores of individual atti‐ tude 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 de‐ pression; individual attitude and expectation predicted diminished general fatigue; and challenging goals and punishment predicted diminished mental fatigue. Challenging goals statistically signifi‐ cantly predicted lower mental fatigue, and mental fatigue statistically significantly predicted de‐ pression 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 selfreported 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.