This study aimed to explore psychometric properties of satisfaction with life scale (SWLS) and psychological capital questionnaire (PCQ-24) in the Lithuanian representative sample (n = 2003, M = 50.67, SD = 17.46). It was significant to validate instruments concerning the fact that Lithuanians’ life satisfaction surveys demonstrated divergent results depending on the assessment tools they used. This study applied the SWLS, created by Diener et al. (1985), and the PCQ-24, created by Luthans et al. (2007). The findings demonstrated the internal consistency of the SWLS instrument, evidencing it as an adequate measure to evaluate satisfaction with life (α = 0.893; TLI = 0.988; NFI = 0.997; RMSEA = 0.059 [0.033–0.088]; CFI = 0.998; SRMR = 0.0077; AVE = 0.764; CR = 0.886). The Lith-PCQ-21 analysis demonstrated the internal consistency of the instrument (α = 0.957) and good fit of the factorial structure (χ² = 2305.383; DF = 185; TLI = 0.915; NFI = 0.920; RMSEA = 0.077 [0.075–0.080]; CFI = 0.925; SRMR = 0.0450; AVE = 0.814; CR = 0.946), evidencing the instrument as an adequate measure to evaluate psychological capital. This research confirmed that both instruments (SWLS and Lith-PCQ-21) not only have an acceptable validity, including construct validity, but they are also interrelated (χ² = 3088.762; DF = 294; TLI = 0.913; NFI = 0.914; RMSEA = 0.070 [0.068–0.073]; CFI = 0.922; SRMR = 0.0469), and can be considered appropriate for monitoring life satisfaction and psychological capital of the Lithuanian population.

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.

This study intended to explore which leisure preferences contribute to mindfulness, psychological capital, and life satisfaction and assess whether mindfulness, psychological capital, and life satisfaction are associated with different leisure preferences. This study applied the Satisfaction with Life Scale (SWLS), the Psychological Capital Questionnaire (PCQ-12), the Mindful Attention Awareness Scale (MAAS), and the instrument to evaluate the prevalence of leisure preferences. A sample consisted of 586 participants, 104 males and 478 females. The mean age of participants was 42.06, SD = 13.29. The results show that respondents who did not spend free time watching television scored higher on life satisfaction, mindfulness, and psychological capital. Participants who preferred attending events scored higher on life satisfaction and psychological capital. Participants who preferred spending time with family as a leisure preference scored significantly higher on life satisfaction, mindfulness, and psychological capital, including PsyCap overall, PsyCap work, PsyCap relationship, and PsyCap health. The findings also reveal that time spent with family is significantly associated with life satisfaction. Besides, males’ life satisfaction was significantly associated with time spent in nature, while females’ satisfaction was associated with spending time with family and participating in events. Males’ mindfulness was significantly associated with book reading, and females’ mindfulness was associated with not watching television. Males’ psychological capital was significantly associated with spending time with family and book reading, and females’ psychological capital was associated with not watching television but spending time with family, participating in events, and spending time in nature. The findings also showed that mindfulness mediated the link between watching television and life satisfaction, and psychological capital mediated links between spending time with family, participating in events, and life satisfaction. The findings demonstrate that life satisfaction is also significantly associated with spending time with family as a leisure preference. This study also revealed a significant negative association between age and spending time with friends or family, evidencing the possible loneliness of elderly respondents. Due to limitations of this study, including sample size and characteristics, cultural context, and research design, the research findings would preferably be regarded thoughtfully.

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.

Some sustainability-related belief systems (or schemas in Cognitive Behavior Therapy) might result in shame, guilt, or denial if a person fails to meet the standards. The psychology of Harmonization relates sustainability to flourishing but not to human misery and delineates flourishing one’s own life. What nature-related cognitive schemas coexist with psychological flourishing? The purpose of this study was to identify the nature-related cognitive schemas that correlate to self-reported psychological flourishing. This paper provides some data on an overall survey (n = 722) that aimed at evaluating the cognitive schemas, strengths, and cognitive abilities of Lithuanian gymnasium students. We applied the Flourishing Scale (FS) of E. Diener alongside several measures to investigate nature-related cognitive/emotional/behavioral variables. The results revealed associations between different nature-related cognitive schemas (experiential, consumeristic, eco-protectionist, and valorist) and psychological flourishing, positive emotional reactions to nature, and spending time in nature. As this study demonstrates only positive or negative relationships among the examined variables, one of the implications for future research is identifying schemas as predictors of behavioral sustainability and creating an experimental or longitudinal design.

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.