Factors contributing to dropping out of adults’ programming e-learning

• Published in: Heliyon Vol. 9; no. 12; p. e22113
• Main Authors: Dirzyte, Aiste, Perminas, Aidas, Kaminskis, Lukas, Žebrauskas, Giedrius, Sederevičiūtė – Pačiauskienė, Živilė, Šliogerienė, Jolita, Suchanova, Jelena, Rimašiūtė – Knabikienė, Romualda, Patapas, Aleksandras, Gajdosikiene, Indre
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.12.2023; Elsevier
• Subjects: 21st century skills; Adult learning; Applications in subject areas; computers; Distance education and e-learning; electronic learning; empirical research; Gender studies; questionnaires; sample size; Adult learning; 21st century skills; Distance education and e-learning; Applications in subject areas; Gender studies
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2023.e22113

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. The graphical abstract is presented in figure 1. figure 1. Graphical abstract. [Display omitted] •Previous studies reported that acquiring computer programming skills is challenging and might result in high dropout rates.•E-learners who completed the courses had higher initial knowledge assessment scores than those who dropped out.•Computer programming e-learners females' personality trait extraversion plays a significant role in dropout.•Reward and recognition are statistically significant in predicting the odds of dropping out in the group of males.•Relatedness frustration significantly affects dropping out of the programming e-learning course.