Computer Programming E-Learners’ Personality Traits, Self-Reported Cognitive Abilities, and Learning Motivating Factors

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.


Introduction
Educational systems worldwide encourage students to engage in programming activities, because learning computer programming improves cognitive skills, including developmental, neurocognitive, and heritability evidence [52]. The CHC theory outlines 16 cognitive abilities, which subsume more than 70 narrow abilities [53]. Most intelligence batteries that are used, including the Wechsler Intelligence Scale (WISC-III) [54], the WASI-II [55], the Stanford-Binet (SB-IV) [56], and the Kaufman Brief Intelligence Test (KBIT) [57], among others, with some regularity subscribe either explicitly or implicitly to the CHC theory [50,58].
There is a debate over the question of whether cognitive abilities can potentially be assessed via self-report measures [58]. Self-report measures are used to assess a wide variety of psychological constructs and are valued for low cost and easy administration, the capability to assess large numbers of individuals simultaneously, and a less anxietyinducing assessment format [59,60]. Research on the validity of self-reports of cognitive abilities suggests that individuals have only limited insight [61,62]. However, some studies have demonstrated high correlations between the self-report of specific cognitive ability areas and performance [63,64] or indicated that individuals can differentiate between distinct cognitive abilities when providing self-ratings [65,66].
In 2014, Jacobs et al. [67] developed the self-report measure of cognitive abilities (SRMCA) as an indicator of the level of cognitive functioning in three key CHC broad ability areas: fluid reasoning (Gf), comprehension-knowledge (Gc), and visual processing (Gv). The fluid reasoning cognitive ability domain represents an individual's ability to reason, form concepts and solve problems that often involve unfamiliar information or procedures. This ability area influences one's ability to draw inferences, solve abstract problems, create solutions to problems, and think conceptually. The comprehensionknowledge cognitive ability domain represents the breadth and depth of an individual's store of general information and acquired verbal knowledge. This ability area influences the extent of one's vocabulary and the ability to answer factual questions and comprehend written and spoken language. The visual processing cognitive ability domain represents an individual's ability to perceive, analyze, synthesize, and think with visual patterns, including storing and recalling visual representations. This ability area influences one's ability to perform tasks such as assembling puzzles, using patterns and designs in art and geography, sensing orientation, and reading maps, charts, graphs, and blueprints [67].
However, computer programming learners' cognitive abilities are under-researched, even though some researchers analyzed programmers' creativity and analytical problemsolving skills [68], and it was concluded that competent programmers are characterized as possessing outstanding cognitive abilities [6,69].

Personality Traits
Individual differences in people's patterns of thinking, feeling, and behaving can be summarized in the Big Five personality domains, namely, extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience, which also cover 15 facets [70]. These Big Five personality domains were extensively analyzed applying the NEO personality inventory [71] or the Big Five Inventory-2 [70] in various contexts, including work, education, or sports. Research demonstrated that neuroticism is related to diminished self-efficacy [72] and higher work-family conflict, while conscientiousness is related to better self-regulation [73].
Many studies analyzed the relationship between personality traits and learning motivation [74][75][76]. Even several decades ago, in 1989, Heaven demonstrated that high school students' achievement motivation is positively related to extraversion and negatively related to impulsiveness and psychoticism [77]. Afterward, research indicated that conscientiousness and extraversion of learners are related to learning achievements [78]. Other studies demonstrated that conscientiousness, extraversion, and openness strongly relate to learning goal orientation, while neuroticism and low extraversion relate to a fear of failure [79]. It was also revealed that only extraversion, conscientiousness, and openness significantly affect students' level of intrinsic motivation to learn [80]. Next, it was found that neuroticism and extroversion are positively related to extrinsic academic motivation, and conscientiousness is positively related to intrinsic academic motivation [81]. Recent research reported that conscientiousness and m-learning readiness are critical antecedents of intrinsic motivation and extrinsic motivation [82].
To sum, many studies confirmed that learners' personality traits have a significant influence on learning motivation [83,84], but the findings are ambiguous. Some studies revealed a positive relationship between neuroticism and intrinsic academic motivation, and between extroversion and conscientiousness and extrinsic academic motivation [85], or a positive relationship between conscientiousness and openness to experience and intrinsic learning motivation, or between extroversion and neuroticism and extrinsic learning motivation [81]. Thus, the links between learning motivating factors and personality are not explored sufficiently.
Likewise, computer programming learners' personality traits are also under-researched, even though there have been many attempts to identify personality traits linked to programming performance [86][87][88][89][90]. A meta-analysis on 19 independent samples (total n = 1695) showed that programming aptitude does not associate with disagreeableness or neuroticism but associates with conscientiousness, openness, and introversion, and these traits explain incremental variance components beyond general mental abilities [89].
In order to fill the gap, this study primarily aimed to identify computer programming e-learners' personality traits, self-reported cognitive abilities and learning motivating factors. Based on the previous research, we hypothesized that:

Hypothesis 1 (H1).
Computer programming e-learners differ in their personality traits from other e-learners;

Hypothesis 2 (H2).
Computer programming e-learners differ in their self-reported cognitive abilities from other e-learners;

Hypothesis 3 (H3).
Computer programming e-learners differ in their learning motivating factors from other e-learners; Hypothesis 4 (H4). Personality traits predict learning motivating factors in both groups of participants and non-participants of computer programming courses; Hypothesis 5 (H5). Self-reported cognitive abilities predict learning motivating factors of participants and non-participants of computer programming courses; Hypothesis 6 (H6). There exist associations between learning motivating factors, personality traits, and self-reported cognitive abilities, but they differ between participants and non-participants of e-learning based computer programming courses.

Sample
In the full sample of 481 participants, a total of 481 participants had no missing data. Because the number of cases with missing values was small, we used the listwise deletion of cases with missing values. Therefore, all analyses were conducted using a sample of 481 individuals. The study's subjects comprised of 33.3% males (n = 160) and 66.7% females (n = 321). The respondents' mean age was 25.07 years (SD = 8.248, 95% CI = 24.33, 25.81, age range =18 to 57 years). A total of 189 (39.3%) of participants studied in e-learning based computer programming courses organized by Turing College, while the rest 292 (60.7%) studied in other Lithuanian Universities. Participation in the study was voluntary, and the participants did not receive any compensation. The procedure was administered online at psytest.online and followed the General Data Protection Regulation (GDPR) guidelines and the requirements of the Helsinki Declaration.

Instruments
This study applied three instruments, the translated Lithuanian version of the learning motivating factors questionnaire [4,7], the translated Lithuanian version of the self-report measure of cognitive abilities (SRMCA) [67], and the translated Lithuanian version of the Big Five Inventory-2 (BFI-2) [70]. To ensure that the Lithuanian items correspond as closely as possible to the English items, the original items of both instruments were translated into Lithuanian and back-translated.
The Self-Report Measure of Cognitive Abilities (SRMCA). To assess self-reported cognitive abilities, we applied the self-report measure of cognitive abilities (SRMCA), developed by Jacobs et al. [67]. The SRMCA is a 25-item scale that evaluates self-reported cognitive functioning in three key CHC broad ability areas: fluid reasoning (Gf), comprehensionknowledge (Gc), and visual processing (Gv). The statements ask to rate how easy or difficult a person usually finds it to perform specific tasks, and when responding to each item, respondents compare themselves to most people of their age; "compared to most people my age, I usually find tasks requiring me to . . . " The response pattern followed a Likert scale ranging from 1 (extremely difficult) to 7 (extremely easy). Cronbach's alpha for the scale in this study was 0.897.
The Learning Motivating Factors Questionnaire. To assess learning motivation, we applied the learning motivating factors questionnaire, developed by Law et al. [4,7]. This 19-items questionnaire measures factors that positively motivate learning and covers several motivational variables listed further. The individual attitude and expectation subscale measures a student's attitude and expectations towards learning. The challenging goals subscale measures perceived challenging goals in learning. The clear direction subscale measures perceived specified direction in learning. The reward and recognition subscale measures perceived positive reinforcements such as reward, appreciation, and encouragement. The punishment subscale measures the perceived negative reinforcement due to punishment. The social pressure and competition subscale measures perceived forces of pressure and competition from peers. The response pattern followed a 6-point Likert scale, ranging from 1 (disagree very much) to 6 (agree very much). Cronbach's alpha for the scale in this study was 0.869.
The Big Five Inventory-2 (BFI-2). Personality traits were measured by the Big Five Inventory-2 (BFI-2), which uses 60 items to hierarchically assess the Big Five personality domains; namely, extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience, and 15 more-specific facet traits [70]. The BFI-2 items are short, descriptive phrases with the shared item: "I am someone who..." (e.g., "is outgoing, sociable," "tends to be quiet"). Respondents rated each item using a 5-point Likert scale ranging from 1 (disagree strongly) to 5 (agree strongly). Cronbach's alpha for the scale in this study was 0.786.

Statistical Analysis
For the data analysis, we used SPSS v.26.0. The structural equation modeling (SEM) was conducted using AMOS v.26.0 and JASP v.0.14.1.0. The model fit was evaluated based on the CFI (comparative fit index), the normed fit index (NFI), the Tucker-Lewis coefficient (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR), whereas the χ 2 was used for descriptive purposes only because it is susceptible to the sample size [91]. The values higher than 0.90 for CFI, NFI, TLI, and values lower than 0.08 for RMSEA and SRMR were considered as indicative of a good fit [92]. In this research, we considered p-values less than 0.05 to be statistically significant [93]. Therefore, we conducted a square root transformation (SQRT) of significantly negatively skewed variables to create normally distributed variables and conduct the CFA analyses.

Results
Descriptive statistics of the learning motivating factors questionnaire subscales in this study are reported in Table 1. Means, standard deviations, and correlations between the BFI-2 subscales in this study are reported in Table 2. Means, standard deviations, and correlations between the SRMCA subscales in this study are reported in Table 3. To test Hypothesis 1 (H1), assuming that computer programming e-learners differ in their personality traits from other e-learners, we conducted the independent samples t-test. The results are displayed in Table 4. The t-test analysis revealed significant differences between groups: non-participants of computer programming e-learning courses demonstrated higher scores (M = 3.36, SD = 0.59) of extraversion than computer programming e-learning courses (M = 3.22, Furthermore, to test Hypothesis 2 (H2), which presumed that computer programming e-learners differ in their self-reported cognitive abilities from other e-learners, we also conducted the independent samples' t-test (Table 5). The t-test analysis revealed no statistically significant differences between the groups. Table 5. Differences in self-reported cognitive abilities between the groups of participants and non-participants of e-learning based computer programming courses. Furthermore, to test Hypothesis 3 (H3), assuming that computer programming elearners differ in their learning motivating factors from other e-learners, we conducted a t-test. The results of the test are displayed in Table 6.

95% CI for Cohen's d
The t-test analysis revealed significant differences between groups of participants and non-participants of e-learning based computer programming courses in the scores of individual attitude and expectation, reward and recognition, and punishment. Non  Furthermore, to test Hypothesis 4 (H4), presuming that personality traits predict learning motivating factors in both groups of participants and non-participants of e-learning based computer programming courses, we conducted a multiple linear regression (forward method) analysis. Statistically significant results in the group of computer programming e-learners are displayed in Table 7. In the group of computer programming e-learners, a significant regression equation was found concerning the motivational factor of individual attitude and expectation, F (1, 180) = 21.373, p < 0.001, with R 2 = 0.106. Predicted individual attitude and expectation was equal to 3.039 + 0.482 (extraversion) points. Individual attitude and expectation increased 0.482 points for each extraversion point (p < 0.001). Thus, extraversion contributed significantly to the model and was a significant predictor of individual attitude and expectation of computer programming e-learners.
A significant regression equation was also found regarding the motivational factor of challenging goals. In model 1, the dependent variable was challenging goals, and the predictor was conscientiousness, F (2, 179) = 50.661, p < 0.001, with R 2 = 0.220. The predicted challenging goals factor was equal to 1.610 + 0.810 (conscientiousness). Challenging goals increased + 0.810 for each conscientiousness point. In model 2, the dependent variable was the individual attitude and expectation, and the predictor was conscientiousness and extraversion, F (2, 179) = 30.600, p = 0.004, with R 2 = 0.255. The predicted challenging goals factor was equal to 1.026 + 0.645 (conscientiousness) + 0.357 (extraversion). Challenging goals increased + 0.645 for each conscientiousness (p < 0.001) point and + 0.357 for each extraversion (p = 0.004) point. Thus, conscientiousness and extraversion were significant predictors of challenging goals of computer programming e-learners.
Next, a significant regression equation was found in relation to a motivational factor of clear direction. In model 1, the dependent variable was clear direction, and the predictor Finally, a significant regression equation was found regarding the motivational factor of social pressure and competition, F (1, 180) = 4.810, p = 0.030. The predicted social pressure and competition was equal to 4.048 − 0.227 (negative emotionality) points. Social pressure and competition decreased 0.227 points for each negative emotionality point. This means that negative emotionality (p = 0.030) was a significant predictor of social pressure and competition of computer programming e-learners.
Next, a multiple linear regression (forward method) was likewise calculated to predict learning motivating factors based on personality traits in the group of respondents, who did not participate in e-learning based computer programming courses. The results of the multiple linear regression models (the dependent variables are learning motivating factors, and the predictors are personality traits), are displayed in Table 8. In the group of non-participants of e-learning based computer programming courses, a significant regression equation was found regarding the motivational factor of individual attitude and expectation, F (1, 220) = 9.866, p = 0.002, with R 2 = 0.043. The predicted individual attitude and expectation was equal to 3.898 + 0.276 (conscientiousness) points. Individual attitude and expectation increased 0.276 points for each conscientiousness point. Thus, conscientiousness (p = 0.002) was a significant predictor of individual attitude and expectation of non-participants of e-learning based computer programming courses.
Next, a significant regression equation was found concerning the motivational factor of challenging goals. In model 1, the dependent variable was challenging goals, and the predictor was conscientiousness, F (2, 219) = 16.021, p < 0.001, with R 2 = 0.068. The predicted challenging goals factor was equal to 2.687 + 0.483 (conscientiousness). Challenging goals increased + 0.483 points for each conscientiousness (p < 0.001) point. In model 2, the dependent variable was challenging goals, and the predictors were conscientiousness and negative emotionality, F (2, 219) = 11.861, p < 0.001, with R 2 = 0.098. The predicted challenging goals factor was equal to 3.837 + 0.400 (conscientiousness) − 0.283 (negative emotionality). Challenging goals increased + 0.400 for each conscientiousness (p = 0.001) point and decreased 0.283 for each negative emotionality (p = 0.008) point. Hence, conscientiousness and negative emotionality were significant predictors of the motivational factor of challenging goals in the group of respondents who did not participate in e-learning based computer programming courses.
A significant regression equation was found in regard to clear direction. In model 1, the dependent variable was clear direction, and the predictor was extraversion, F (2, 219) = 19.116, p < 0.001, with R 2 = 0.080. Predicted clear direction was equal to 3.694 + 0.395 (extraversion). Clear direction increased + 0.395 for each extraversion point. In model 2, the dependent variable was clear direction, and the predictors were extraversion and agreeableness, F (2, 219) = 12.714, p < 0.001, with R 2 = 0.104. Predicted clear direction was equal to 3.286 + 0.300 (extraversion) + 0.217 (agreeableness). Clear direction increased + 0.300 for each extraversion (p = 0.002) point and + 0.217 for each agreeableness (p = 0.016) point. Thus, extraversion and agreeableness were significant predictors of clear direction in group of other e-learners who did not participate in e-learning based computer programming courses.
Lastly, a significant regression equation was found in regard to the reward and recognition motivational factor, F (1, 220) = 9.011, p < 0.001, with R 2 = 0.039. The predicted reward and recognition was equal to 4.019 + 0.284 (conscientiousness) points. Reward and recognition increased 0.284 points for each conscientiousness (p = 0.003) point. This means that conscientiousness significantly predicted the motivational factor of reward and recognition in the group of non-participants of e-learning based computer programming courses.
Furthermore, to test Hypothesis 5 (H5), presuming that self-reported cognitive abilities predict learning motivating factors of computer programming e-learners, we conducted a multiple linear regression (forward method), using learning motivating factors as the criterion, and self-reported cognitive abilities as the predictors. Statistically significant results in the group of computer programming e-learners are displayed in Table 9. In the group of respondents participating in e-learning based computer programming courses, a significant regression equation was found regarding the motivational factor of individual attitude and expectation, F (1, 181) = 8.060, p = 0.005, with R 2 = 0.043. The predicted individual attitude and expectation was equal to 3.577 + 0.197 (comprehensionknowledge) points. Individual attitude and expectation increased 0.197 points for each comprehension-knowledge point (p = 0.005). Thus, comprehension-knowledge was a significant predictor of individual attitude and expectation of computer programming e-learners.
Next, a significant regression equation was found concerning the motivational factor of challenging goals. In model 1, the dependent variable was challenging goals, and the predictor was fluid reasoning, F (2, 180) = 41.361, p < 0.001, with R 2 = 0.186. The predicted challenging goals factor was equal to 1. Finally, a significant regression equation was found concerning the motivational factor of social pressure and competition, F (1, 181) = 6.212, p = 0.014, with R 2 = 0.033. The predicted social pressure and competition was equal to 1.951 + 0.256 (visual processing) points. Social pressure and competition increased 0.256 points for each visual processing (p = 0.014) point, which means that visual processing was a significant predictor of social pressure and competition of participants of e-learning based computer programming courses.
Then, we conducted a multiple linear regression (forward method) in the group of non-participants of computer programming e-learning courses, using learning motivating factors as the criterion, and self-reported cognitive abilities as the predictors. The results are displayed in Table 10. In the group of respondents who did not participate in e-learning based computer programming courses, a significant regression equation was found concerning the motivational factor of individual attitude and expectation, F (1, 220) = 8.739, p = 0.003, with R 2 = 0.038. The predicted individual attitude and expectation was equal to 3.961 + 0.168 (fluid reasoning) points. Individual attitude and expectation increased 0.168 points for each fluid reasoning (p = 0.003) point. Thus, fluid reasoning was a significant predictor of individual attitude and expectation of other e-learners who did not participate in computer programming courses.
Next, a significant regression equation was found in regard to the motivational factor of challenging goals, F (1, 220) = 12.212, p = 0.001, with R 2 = 0.053. The predicted challenging goals was equal to 2.899 + 0.273 (fluid reasoning) points. Challenging goals increased 0.273 points for each fluid reasoning (p = 0.001) point. Therefore, fluid reasoning was a significant predictor of challenging goals of non-participants of e-learning based computer programming courses.
Likewise, a significant regression equation was found concerning the motivational factor of clear direction, F (1, 220) = 5.491, p = 0.020, with R 2 = 0.024. The predicted clear direction was equal to 4.243 + 0.146 (visual processing) points. Clear direction increased 0.146 points for each visual processing (p = 0.020) point. So, visual processing was a significant predictor of clear direction in the group of other e-learners who did not participate in computer programming courses.
After all, a significant regression equation was found regarding the reward and recognition motivational factor, F (1, 220) = 10.664, p = 0.001, with R 2 = 0.046. The predicted reward and recognition was equal to 3.750 + 0.225 (visual processing) points. Reward and recognition increased 0.225 points for each visual processing (p = 0.001) point. So, visual processing was a significant predictor of reward and recognition in the group of non-participants of e-learning based computer programming courses.
Furthermore, to test Hypothesis 6 (H6), which assumed that there exist associations between learning motivating factors, personality traits, and self-reported cognitive abilities, but they differ between participants and non-participants of e-learning based computer programming courses, we conducted several SEM analyses. Firstly, based on previous results, we created a model on associations between learning motivating factors, selfreported cognitive abilities, and personality traits in the group of computer programming e-learners. Standardized results of the model are presented in Figure 1. The model fit was evaluated based on the CFI (Comparative Fit Index), the normed fit index (NFI), the Tucker-Lewis coefficient (TLI), and the root mean square error of approximation (RMSEA), whereas the χ 2 was used for descriptive purposes only [91]. As mentioned above, the values higher than 0.90 for CFI, NFI, and TLI, and the values lower than 0.08 for RMSEA are considered as indicative of a good fit [92]. Findings revealed that the fit of the model was good, was a significant predictor of challenging goals of non-participants of e-learning based computer programming courses. Likewise, a significant regression equation was found concerning the motivational factor of clear direction, F (1, 220) = 5.491, p = 0.020, with R 2 = 0.024. The predicted clear direction was equal to 4.243 + 0.146 (visual processing) points. Clear direction increased 0.146 points for each visual processing (p = 0.020) point. So, visual processing was a significant predictor of clear direction in the group of other e-learners who did not participate in computer programming courses.
After all, a significant regression equation was found regarding the reward and recognition motivational factor, F (1, 220) = 10.664, p = 0.001, with R 2 = 0.046. The predicted reward and recognition was equal to 3.750 + 0.225 (visual processing) points. Reward and recognition increased 0.225 points for each visual processing (p = 0.001) point. So, visual processing was a significant predictor of reward and recognition in the group of nonparticipants of e-learning based computer programming courses.
Furthermore, to test Hypothesis 6 (H6), which assumed that there exist associations between learning motivating factors, personality traits, and self-reported cognitive abilities, but they differ between participants and non-participants of e-learning based computer programming courses, we conducted several SEM analyses. Firstly, based on previous results, we created a model on associations between learning motivating factors, self-reported cognitive abilities, and personality traits in the group of computer programming e-learners. Standardized results of the model are presented in Figure 1. The model fit was evaluated based on the CFI (Comparative Fit Index), the normed fit index (NFI), the Tucker-Lewis coefficient (TLI), and the root mean square error of approximation (RMSEA), whereas the χ 2 was used for descriptive purposes only [91]. As mentioned above, the values higher than 0.90 for CFI, NFI, and TLI, and the values lower than 0.08 for RMSEA are considered as indicative of a good fit [92].   Then, we created a model on associations between learning motivating factors, selfreported cognitive abilities, and personality traits in the group of non-participants of computer programming e-learning based courses. Standardized results of the model are presented in Figure 2. Findings revealed that the fit of the model was good, X 2   Afterwards, based on previous analyses, we examined an alternative model on the associations between learning motivating factors, self-reported cognitive abilities, and personality traits in both groups of participants and non-participants of computer programming e-learning based courses. Standardized results of the model are presented in Figure 3. Afterwards, based on previous analyses, we examined an alternative model on the associations between learning motivating factors, self-reported cognitive abilities, and personality traits in both groups of participants and non-participants of computer programming e-learning based courses. Standardized results of the model are presented in Figure 3.
The model fit was evaluated based on the comparative fit index (CFI), the normed fit index (NFI), the Tucker-Lewis coefficient (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). The values higher than 0.90 for CFI, NFI, and TLI, and values lower than 0.08 for RMSEA and SRMR were considered as indicative of a good fit [92]. The results showed that model fit was good  The model fit was evaluated based on the comparative fit index (CFI), the normed fit index (NFI), the Tucker-Lewis coefficient (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). The values higher than 0.90 for CFI, NFI, and TLI, and values lower than 0.08 for RMSEA and SRMR were considered as indicative of a good fit [92]. The results showed that model fit was good χ²  Table 11. Table 11. Scalar estimates of the model on associations between learning motivating factors, self-reported cognitive abilities, and personality traits in both groups of participants and non-participants of computer programming e-learning based courses.   To sum, the SEM analysis showed that personality traits (namely, extraversion, conscientiousness, and reverted negative emotionality) statistically significantly predict learning motivating factors (namely, individual attitude and expectation, and clear direction), and 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.

Discussion
This study was the first to explore computer programming e-learners' personality traits, self-reported cognitive abilities, and learning motivating factors compared with other e-learners. The assessment of learning motivating factors was based on a model of extrinsic and intrinsic learning motivating factors, developed by Law et al. [4]. It was significant to identify the specifics in computer programing learning because educational systems around the world encourage students to engage in programming activities, which improve cognitive skills, including creativity, reasoning, and mathematical skills [1]. Furthermore, acquiring computer programming skills also ensures large work possibilities, as hiring managers prefer candidates who know programming languages [2,3]. However, computer programming learning is one of the most challenging tasks, indicating high dropout rates or failures [5]. Thus, it was significant to explore the factors that researchers point out as related to programming learning outcomes [6,8,68,69], and this study aimed to identify computer programming e-learners' personality traits, self-reported cognitive abilities and learning motivating factors.

Computer Programming E-Learners Demonstrated Significantly Lower Extraversion Scores Than Non-Participants of E-Learning Based Computer Programming Courses
In this study, we assumed that computer programming e-learners differ in their personality traits from other e-learners. Thus, we compared the scores of personality traits based on the Big Five Inventory-2 categories in the groups of participants and non-participants of e-learning based computer programming courses. The results partially confirmed this hypothesis: a t-test analysis revealed significant differences between the groups: nonparticipants of computer programming e-learning courses demonstrated higher scores of personality trait extraversion than participants of computer programming e-learning courses. However, there were no significant differences in the scores of agreeableness, conscientiousness, neuroticism, and openness to experience. These results align with some previous research suggesting that computer programming e-learners might demonstrate lower scores in extraversion [86][87][88][89][90]. However, it is unclear why no significant differences were found in other traits, namely, conscientiousness or open-mindedness, as indicated by some previous research [89].
Interestingly, a magnetic resonance imaging (MRI) study by Gardini et al. [94] indicated that individual differences in the central personality dimensions might reflect structural variance in specific brain areas. Researchers found that personality dimension novelty seeking correlated with grey matter volume in frontal and posterior cingulate regions, harm avoidance showed a negative correlation with grey matter volume in orbitofrontal, occipital and parietal structures, and reward dependence was negatively correlated with grey matter volume in the caudate nucleus and the rectal frontal gyrus. Next, personality dimension persistence was positively correlated with grey matter volume in the precuneus, paracentral lobule, and parahippocampal gyrus [94]. Furthermore, Kabbara et al. [95] found a significant relationship between neuroticism and the dynamic variability of the temporal lobe regions. They highlighted the importance of tracking the dynamics of functional brain networks to improve understanding of the neural substrates of personality [95]. Furthermore, in a study by Lewis et al. [96], conscientiousness trait scores were positively related to brain cortical thickness in a range of regions, including the bilateral parahippocampal gyrus, bilateral fusiform gyrus, left cingulate gyrus, right medial orbitofrontal cortex, and the left dorsomedial prefrontal cortex [96]. Concerning various findings [94][95][96][97][98][99], it can be suggested that computer learners' personalities need further investigation, especially in establishing links to computer programming e-learners' brain regions.

No Significant Differences Were Found in the Scores of Self-Reported Cognitive Abilities between the Groups of Participants and Non-Participants of E-Learning Based Computer Programming Courses
Furthermore, we presumed that computer programming e-learners differ in their cognitive abilities from other e-learners. Thus, we compared the scores of self-reported cognitive abilities based on the SRMCA, which indicates the level of cognitive functioning in three key ability areas: fluid reasoning, comprehension-knowledge, and visual processing [67]. The results did not confirm this hypothesis: a t-test analysis revealed no statistically significant differences in self-reported cognitive abilities between the groups of participants and non-participants of e-learning based computer programming courses. These findings contradict previous research indicating the excellent cognitive skills of computer programmers, including analytical, problem-solving, and mathematical skills [6,68,69]. These results might point to some methodological issues. This study was based on selfreported measures, and the possible significance of objective, including neurocognitive, indicators was omitted. Thus, even though the authors of the SRMCA indicate that the instrument measures the level of cognitive functioning in three key ability areas of fluid reasoning, comprehension-knowledge, and visual processing [67], it can be presumed that the SRMCA measures only self-efficacy, related to these areas. Therefore, the results of this study should be taken with caution, and we strongly suggest applying neurocognitive methods in future research.
Interestingly, a study by Breukelaar et al. [100] provided evidence that changes in the functional organization of the cognitive control brain network occur despite the absence of neurodevelopment, aging, or targeted cognitive training effects and can modulate cognitive performance in early to mid-adulthood. Comprehensive research by Oschwald et al. [101] provided guidance for future researchers and demonstrated that despite several studies reporting correlations between specific brain regions and specific cognitive abilities (e.g., between structures of the medial temporal lobe and episodic memory), the number of studies presenting converging evidence is small. Concerning these and other studies [100][101][102][103][104][105][106], it can be concluded that computer learners' cognitive abilities need further investigation, especially in establishing precise links to learners' structural and functional brain properties.

Computer Programming E-Learners Demonstrated Significantly Lower Scores of Motivating Factors of Individual Attitude and Expectation, Reward and Recognition, and Punishment
Next, we assumed that computer programming e-learners differ in their learning motivating factors from other e-learners. Therefore, we compared the scores of learning motivating factors in the groups of participants and non-participants of e-learning based computer programming courses. The results partially confirmed this hypothesis. A t-test analysis revealed significant differences between the groups of participants and nonparticipants of e-learning based computer programming courses in the scores of individual attitude and expectation, reward and recognition, and punishment. Non-participants of e-learning based computer programming courses demonstrated higher scores in individual attitude and expectation in comparison with computer programming e-learners. Nonparticipants also demonstrated higher scores in reward and recognition than computer programming e-learners. Finally, non-participants demonstrated higher scores in punishment than those who participated in e-learning based computer programming courses. However, no significant differences were found in a clear direction, challenging goals, or social pressure and competition in the groups of participants and non-participants of e-learning based computer programming courses. Previous research [2,42] can moderately support these findings, but it is unclear why no significant differences were found in clear direction and challenging goals between computer programming and other e-learners [41].

Personality Traits Predict Learning Motivating Factors in Both Groups of Participants and Non-Participants of Computer Programming Courses
Next, we presumed that personality traits predict learning motivating factors in both groups of participants and non-participants of computer programming courses. Thus, we conducted a multiple linear regression using learning motivating factors as the criterion and personality traits as predictors in groups of respondents participating and not participating in e-learning based computer programming courses. The results partially confirmed this hypothesis. It showed that in the group of respondents participating in computer programming courses, 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 diminished motivational factor of punishment; negative emotionality was a significant predictor of social pressure and competition. On the other hand, in the group of non-participants of e-learning based computer programming courses, conscientiousness was a significant predictor of individual attitude and expectation; conscientiousness and negative emotionality were significant predictors of challenging goals; extraversion and agreeableness were significant predictors of clear direction; conscientiousness significantly predicted reward and recognition. These results to some extent are in line with previous studies which analyzed the links between personality traits and learning motivation [74][75][76][77]. The findings support previous reports indicating that conscientiousness, extraversion, and openness significantly affect students' level of intrinsic motivation to learn [80], and that these personality traits relate to learning goal orientation, while neuroticism and low extraversion relates to a fear of failure [78,79]. This study also aligns with previous findings, indicating that conscientiousness is positively related to intrinsic academic motivation [81,85], but it does not support previous findings that neuroticism and extroversion are positively related to extrinsic academic motivation. To sum, this study contributed to previous research that confirmed that learners' personality traits significantly influence learning motivation [83,84]. However, the findings in both sample groups are ambiguous, and the different patterns of links between personality traits and learning motivating factors in groups of participants and non-participants of e-learning based computer programming courses requires additional examination.

Self-Reported Cognitive Abilities Predict Learning Motivating Factors in Both Groups of Participants and Non-Participants of Computer Programming Courses
Furthermore, we assumed that self-reported cognitive abilities predict learning motivating factors in both groups of participants and non-participants of computer programming courses. Consequently, we conducted a multiple linear regression using learning motivating factors as the criterion and self-reported cognitive abilities as predictors in groups of respondents participating and not participating in e-learning based computer programming courses. The results partially confirmed this hypothesis. It demonstrated that in the group of computer programming e-learners, comprehension-knowledge was a significant predictor of individual attitude and expectation; fluid reasoning and comprehensionknowledge were significant predictors of challenging goals; comprehension-knowledge was a significant predictor of clear direction; visual processing was a significant predictor of social pressure and competition. On the other hand, in the group of non-participants of e-learning based computer programming courses, fluid reasoning was a significant predictor of individual attitude and expectation; fluid reasoning was a significant predictor of challenging goals; visual processing was a significant predictor of clear direction; visual processing was a significant predictor of reward and recognition. These findings are partially in line with some previous research [6,68,69,[86][87][88][89][90]. However, the different patterns of links between self-reported cognitive abilities and learning motivating factors in groups of participants and non-participants of computer programming e-courses are unclear and need further investigation.

There Exist Associations between Learning Motivating Factors, Personality Traits, and Self-Reported Cognitive Abilities in Groups of Participants and Non-Participants of E-Learning Based Computer Programming Courses
Additionally, we assumed that there exist associations between learning motivating factors, personality traits, and self-reported cognitive abilities, but they differ between participants and non-participants of e-learning based computer programming courses. Thus, we conducted several SEM analyses to identify significant associations between the study variables in both sample groups. The findings revealed some statistically significant associations between the latent variables of personality traits, self-reported cognitive abili-ties, and learning motivating factors. This study also demonstrated that personality traits (namely, extraversion, conscientiousness, and reverted negative emotionality) statistically significantly predicts learning motivating factors (namely, individual attitude and expectation, and clear direction), and 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. These findings can be moderately supported by previous research which analyzed links between learning motivating factors, personality traits, and self-reported cognitive abilities [6,8,23,39,68,69,[74][75][76][80][81][82][83][84][85][86][87][88][89][90]. This study at least modestly contributed to better understanding of the complexity of associations between intrinsic and extrinsic learning motivating factors, personality, and self-reported cognitive abilities. However, to comprehend the genuine links between e-learners learning motivation, personality traits, and cognitive abilities, it would be advantageous to apply neurocognitive methods, as self-reported measures, especially concerning cognitive abilities, might generate inaccurate results.
To sum, this study added to extensive research on learning motivation, personality, and cognitive abilities [4,5,8,23,39,68,69,74,76,78,89,90,. The findings revealed some distinct features of computer programming e-learners' learning motivation, personality traits, and self-reported cognitive abilities. However, the results need further investigation, especially applying neurocognitive methods to assess genuine links between cognitive capabilities, personality traits, and learning.

Limitations and Future Directions
There are several limitations of this study that should be considered. First, as the objective cognitive capabilities were not measured, bias might occur due to the use of selfreport measures only and omitting the objective indicators. Second, the findings should be regarded with caution as the data were collected online and based on self-observations. Third, the research samples were not representative but random, suggesting the necessity to analyze representative samples of e-learners; thus, generalizations should be made with caution. Fourth, this study was conducted in Lithuania, and the results might reflect the specifics of this area. Finally, the findings suggest a necessity for longitudinal or experimental research design, and we strongly suggest applying neurocognitive methods in future research, as self-report measures may generate inaccurate results.

Practical Implications
Computer programming competencies are among the fundamental skills that specialists are expected to acquire in the era of fast technology development [3], and hiring managers favor applicants who know programming languages and demonstrate computational literacy [2]. Educational organizations around the world encourage students to engage in programming learning [1], even though programming is acknowledged as one of the most difficult learning tasks [4], which relates to the high dropout rates and failures [5]. Research indicates that learning accomplishments might depend not only on numerous neurobiological factors, including genetic factors [9], cerebral metabolism [11,12], neurotrophic factors [14], neurotransmitters [13], hippocampal region [10], and the interactions between the distributed brain areas and neuronal networks [15][16][17][18][19][20]. Learning achievements might also depend on psychological factors, including learners' characteristics [6] and learning motivation [4,7,8]. Thus, it was essential to identify links between computer programming e-learners' personality traits, self-reported cognitive abilities, and learning motivating factors. The results revealed distinctive features of computer programming elearners' learning motivating factors, personality traits, and self-reported cognitive abilities. However, it also strongly suggested to apply neurocognitive methods in future research, as self-report measures may generate inaccurate results. Nonetheless, this research implies that education policymakers, neuroscience researchers, and educators, to promote learners' developing computer programming skills, should target computer programming e-learners' cognitive abilities, which are linked to personality traits and learning motivation.

Conclusions
This study aimed to identify computer programming e-learners' personality traits, selfreported cognitive abilities, and learning motivating factors in comparison with a group of other e-learners. Computer programming e-learners demonstrated significantly lower extraversion scores than non-participants of e-learning based computer programming courses. Next, computer programming e-learners demonstrated significantly lower scores of motivating factors of individual attitude and expectation, reward and recognition, and punishment. In the group of respondents participating in computer programming courses, 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. Next, in the group of computer programming e-learners, 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; visual processing was a significant predictor of social pressure and competition. The findings also revealed some statistically significant associations between the latent variables of personality traits, self-reported cognitive abilities, and learning motivating factors. This study 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). However, 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. However, as this study applied self-reported measures, the results should be taken with caution. We strongly suggest applying neurocognitive methods in future research to explore the genuine links between computer programming e-learners' learning motivation, personality, and cognitive capabilities.