Measuring the Programming Self-Efficacy of Electrical and Electronics Engineering Students
Contribution: This article has shown that self-efficacy in performing complex computer programming tasks and the self-regulation of electrical and electronics engineering undergraduate students varies with respect to the class standing and prior experience in computer programming. Background: Comput...
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| Veröffentlicht in: | IEEE transactions on education 2020-08, Vol.63 (3), p.216-223 |
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| creator | Kittur, Javeed |
| description | Contribution: This article has shown that self-efficacy in performing complex computer programming tasks and the self-regulation of electrical and electronics engineering undergraduate students varies with respect to the class standing and prior experience in computer programming. Background: Computer programming is an essential skill that all engineers must possess, as most industries require engineers to own this skill. Prior studies discuss programming self-efficacy (PSE) measures; however, these studies do not specifically measure the PSE of electrical and electronics engineering major in the Indian context. Research Questions: The overarching research question of this article is: What factors related to participants' demographics, vicarious experiences, and performance accomplishments influence the PSE of electrical and electronics engineering students in India? Methodology: To answer the above research question, a study is conducted using quantitative methods in engineering education research. This article focuses on the development of a survey instrument to measure the PSE of electrical and electronics engineering students of India. The survey includes a total of 31 items, content validity and face validity were established. An exploratory factor analysis was conducted, and the final two factors are "basic programming tasks and dependence" and "complex programming tasks and self-regulation." A multiple regression analysis was performed to find the best fitting model for the two factors. Findings: The regression model with "basic programming tasks and dependence" was not statistically significant and the regression model with "complex programming tasks and self-regulation" was found to be statistically significant with predictors class standing and prior programming experience. |
| doi_str_mv | 10.1109/TE.2020.2975342 |
| format | Article |
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Background: Computer programming is an essential skill that all engineers must possess, as most industries require engineers to own this skill. Prior studies discuss programming self-efficacy (PSE) measures; however, these studies do not specifically measure the PSE of electrical and electronics engineering major in the Indian context. Research Questions: The overarching research question of this article is: What factors related to participants' demographics, vicarious experiences, and performance accomplishments influence the PSE of electrical and electronics engineering students in India? Methodology: To answer the above research question, a study is conducted using quantitative methods in engineering education research. This article focuses on the development of a survey instrument to measure the PSE of electrical and electronics engineering students of India. The survey includes a total of 31 items, content validity and face validity were established. An exploratory factor analysis was conducted, and the final two factors are "basic programming tasks and dependence" and "complex programming tasks and self-regulation." A multiple regression analysis was performed to find the best fitting model for the two factors. Findings: The regression model with "basic programming tasks and dependence" was not statistically significant and the regression model with "complex programming tasks and self-regulation" was found to be statistically significant with predictors class standing and prior programming experience.</description><identifier>ISSN: 0018-9359</identifier><identifier>EISSN: 1557-9638</identifier><identifier>DOI: 10.1109/TE.2020.2975342</identifier><identifier>CODEN: IEEDAB</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Alliances ; Computer programming ; Dependence ; Electric variables measurement ; Electrical engineering ; Electronics ; Engineering ; Engineering Education ; Engineering students ; Engineers ; Factor analysis ; Foreign Countries ; gender ; Instruments ; Java ; Measures (Individuals) ; Multiple regression analysis ; Predictor Variables ; Prior Learning ; Private Colleges ; Programming ; Programming profession ; programming self-efficacy (PSE) ; Questions ; regression ; Regression models ; Self Efficacy ; Self Management ; Statistical significance ; student background ; Student Characteristics ; Students ; Task analysis ; Task complexity ; Teaching methods ; Undergraduate Students ; Undergraduate study</subject><ispartof>IEEE transactions on education, 2020-08, Vol.63 (3), p.216-223</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Background: Computer programming is an essential skill that all engineers must possess, as most industries require engineers to own this skill. Prior studies discuss programming self-efficacy (PSE) measures; however, these studies do not specifically measure the PSE of electrical and electronics engineering major in the Indian context. Research Questions: The overarching research question of this article is: What factors related to participants' demographics, vicarious experiences, and performance accomplishments influence the PSE of electrical and electronics engineering students in India? Methodology: To answer the above research question, a study is conducted using quantitative methods in engineering education research. This article focuses on the development of a survey instrument to measure the PSE of electrical and electronics engineering students of India. The survey includes a total of 31 items, content validity and face validity were established. 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| subjects | Alliances Computer programming Dependence Electric variables measurement Electrical engineering Electronics Engineering Engineering Education Engineering students Engineers Factor analysis Foreign Countries gender Instruments Java Measures (Individuals) Multiple regression analysis Predictor Variables Prior Learning Private Colleges Programming Programming profession programming self-efficacy (PSE) Questions regression Regression models Self Efficacy Self Management Statistical significance student background Student Characteristics Students Task analysis Task complexity Teaching methods Undergraduate Students Undergraduate study |
| title | Measuring the Programming Self-Efficacy of Electrical and Electronics Engineering Students |
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