Computer Programming E-Learners’ Personality Traits, Self-Reported Cognitive Abilities, and Learning Motivating Factors
| Autor: | Lukas Kaminskis, Aidas Perminas, Romualda Rimasiute-Knabikiene, Giedrius Zebrauskas, Aiste Dirzyte, Aivaras Vijaikis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Agreeableness
media_common.quotation_subject Motyvacija / Motivation Neurosciences. Biological psychiatry. Neuropsychiatry Computer programming E-learning Article Structural equation modeling Developmental psychology Lietuva (Lithuania) motivation Personality Big Five personality traits e-learning media_common Kompiuterinis projektavimas Extraversion and introversion Cognitive abilities General Neuroscience Pažintiniai gebėjimai Cognition Conscientiousness Psichologija / Psychology cognitive abilities computer programming Nuotolinis mokymas ir mokymasis / Distance teaching and learning personality Psychology Neurocognitive RC321-571 |
| Zdroj: | Brain Sciences, Vol 11, Iss 1205, p 1205 (2021) Brain sciences 2021, 11, 1205, p. 1-24. Brain Sciences Volume 11 Issue 9 |
| ISSN: | 2076-3425 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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