June 24, 2017
June 24, 2017
June 28, 2017
Educational Research and Methods
Title: The relationship between engineering students’ self-efficacy beliefs and their experience learning computer programming: A sequential explanatory mixed-methods investigation
A core outcome for engineering education is the ability for engineers to formulate and solve engineering problems . One of the many ways by which engineers solve problems is by using computer programming. There is evidence in literature that identify factors impacting students’ self-efficacy beliefs in the context of programming [2,3]. However, there is little evidence on the relationship between students experience of learning programming and how it shapes their self-efficacy beliefs. Since programming is considered difficult to learn , it would be insightful to study how the students’ self-efficacy beliefs evolve as students learn how to program. Students’ self-efficacy beliefs may affect their decision to persist in engineering. Understanding how students experience programming and how this experience affects their self-efficacy beliefs can help programming educators design appropriate interventions to counter the demotivating factors related to self-efficacy.
We are planning a sequential explanatory mixed methods study . The initial quantitative phase will provide a general understanding of the problem. The subsequent qualitative phase will explore participants’ views in detail . The overarching mixed-methods research question for this study is, "What is the relationship between engineering students’ self-efficacy beliefs and their experience learning computer programming?" The quantitative research question is, "How do students’ self-efficacy beliefs about programming and about engineering in general change after taking an introductory computer programming course?" The qualitative phase will address the question, "Why do students’ self-efficacy beliefs change as a result of taking an introductory programming course?"
We will use stratified random sampling to collect data from 12 institutions in the United States that require a computer programming course for undergraduate engineering students (2 baccalaureate institutions, 2 master’s universities, and 8 doctoral universities). The data will be collected so that it is representative of race, gender, and ethnicity of the students. The validated Computer Programming Self-Efficacy Scale  will be administered as a pre- and post-test to collect quantitative data. The qualitative phase will be conducted using a multiple case-study research design. During this phase, two of each type of institution will be selected as sites for conducting face-to-face observations and interviews. The data collection protocols will be developed using Bandura’s Social Cognitive Theory and results from the quantitative phase. This data will be collected using maximum variation sampling and will be analyzed using thematic analysis. The results from the quantitative and qualitative strands of the study will be integrated to understand the results of the study. This work-in-progress paper will be discussed at the conference and will be refined based on the feedback from the broader research community.
1. Accreditation Board of Engineering and Technology. (1999). Criteria for accrediting engineering programs. 2. Askar, P., & Davenport, D. (2009). An investigation of factors related to self-efficacy for java programming among engineering students. Turkish Online Journal of Educational Technology, 8(1), 26–32. 3. Jegede, P. O. (2009). Predictors Of Java Programming Self Efficacy Among Engineering Students In A Nigerian University. arXiv:0909.0074 [cs]. Retrieved from http://arxiv.org/abs/0909.0074 4. Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13, 137–172. 5. Creswell, J. W., & Clark, V. L. P. (2011). Designing and Conducting Mixed Methods Research. SAGE. 6. Ramalingam, V., & Wiedenbeck, S. (1998). Development and Validation of Scores on a Computer Programming Self-Efficacy Scale and Group Analyses of Novice Programmer Self-Efficacy. Journal of Educational Computing Research, 19(4), 367–381. https://doi.org/10.2190/C670-Y3C8-LTJ1-CT3P
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