Measuring the Programming Self-Efficacy of Electrical and Electronics Engineering Students
| Autor: | Javeed Kittur |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Class (computer programming)
business.industry Computer science 05 social sciences Computer programming Electrical engineering 050301 education Engineering education research Context (language use) Education Engineering education Content validity Electrical and Electronic Engineering business 0503 education Research question Face validity |
| Zdroj: | IEEE Transactions on Education. 63:216-223 |
| ISSN: | 1557-9638 0018-9359 |
| DOI: | 10.1109/te.2020.2975342 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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