Research Article
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Adapting Computer Programming Self-Efficacy Scale and Engineering Students’ Self-Efficacy Perceptions

Year 2014, Volume: 1 Issue: 1, 20 - 31, 01.06.2014
https://doi.org/10.17275/per.14.02.1.1

Abstract

Students
might have different type and different level of perceptions: Positive or
negative perceptions on programming; a perception on benefit of programming,
perceptions related to difficulties of programming process etc.  The perception of student on their own
competence is defined as self-efficacy. Based on the discussions reported in
literature, measuring self-efficacy is certainly necessary and, in this
context, is highly important in order to develop new pedagogical methods to
address the problems related to computer programming. The purpose of this study
is to adapt a well-known self-efficacy scale and determine engineering
student’s C++ computer programming self-efficacy levels. The sample group
consists of 378 engineering students. In order to test the validity of the
scale, an exploratory factor analysis has been conducted and item
discriminative power has been evaluated. The reliability of the scale, on the
other hand, has been justified using the internal consistency level. The
results indicate that the scale is reliable and valid, and it can be used to
measure the self-efficacy of the engineering student in Turkish cultural
environment. Furthermore, it is revealed that the level of self-efficacy
perception of the students is middling and it does not show any meaningful
difference between genders. On the other hand, self-efficacy perception of
students in computer engineering is found to be higher than that of the
students in electrical-electronics engineering.

Thanks

A part of this article presented on 3th Word Conference on Information Technology

References

  • Anastasiadou, S.D., & Karakos, A.S. (2011). The beliefs of electrical and computer engineering students’ regarding computer programming. The International Journal of Technology, Knowledge and Society, 7(1):37-51.
  • Askar, P., &, Davenport, D. (2009). .An investigation of factors related to self-efficacy for java Programming among engineering students .The Turkish Online Journal of Educational Technology TOJET. 8(1): 26-32
  • Austin, H.S. (1987). Predictors of Pascal Programming Achievement for Community College Students. Proceedings of the eighteenth SIGCSE technical symposium on Computer science education, Missouri, United States, 161-164.
  • Bandura, A. (1986), Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice Hall, Englewood Cliffs, NJ.
  • Bergin, S., & Reilly, R. (2005). The influence of motivation and comfort-level on learning to program. In University of Sussex, Brighton, UK.
  • Boehm, B. W. (1981). Software engineering economics. Englewood Cliffs, NJ: Prentice-Hall.
  • Brooks, F. P. (1995). The mythical man-month: Essays on software engineering anniversary edition. Reading, MA: Addison-Wesley.
  • Büyüköztürk, Ş. (2002). Sosyal bilimler için veri analizi el kitabı [Data analysis handbook for social sciences]. Ankara: PegemA Press.
  • Caspersen, M.E., & Kolling M. (2009). STREAM: A first programming process. ACT Transaction on Computing Education, 9,1-29.
  • Deniz, K.Z. (2007). The adaptation of psychological scales. Ankara University, Journal of Faculty of Educational Sciences, 40(1), 1-16.
  • Dreyfus, H., & Dreyfus, S. (1986). Mind over machine: The power of human intuition and expertise in the era of the computer. New York: Free Press.
  • Erdogan, Y., Aydin, E., & Kabaca, YT. (2008). Exploring the Psychological Predictors of Programming Achievement. Journal of Instructional Psychology, 35(3), 264-270.
  • Eroglu, A. (2008). Faktör analizi [Factor Analyze]. Ş. Kalaycı (Ed.), SPSS Uygulamalı çok değişkenli istatistik teknikleri [Applied multivariate statistical technique] (ss. 321-331). Ankara: Asil Yayın Dağıtım.
  • Fang, X. (2012). Application of the participatory method to the computer fundamentals course, Affective Computing and Intelligent Interaction. Advances in Intelligent and Soft Computing, 137, 185-189.
  • Garner, S. (2009). Learning to program from Scratch. 9th IEEE Int. Conference on Advanced Learning Technologies, 451-452, Doi: 10.1109/ICALT.2009.50.
  • Gomes, A., & Mendes, A., J.(2007). Learning to program-difficulties and solutions. International Conference on Engineering Education, ICEE. 3-7 Temmuz, Coimbra, Portugal. Available at http://ineer.org/Events/ICEE2007/papers/411.pdf
  • Gülbahar, Y. & Büyüköztürk, Ş. (2008). Değerlendirme tercihleri ölçeğinin Türkçeye uyarlanması [Adaptation of evaluation choices scale into Turkish]. Hacettepe University Faculty of Education Journal, 35, 148-161.
  • Hambleton, R.K., & Patsula, L. (1999). Increasing the validity of adapted tests: myths to be avoided an guidelines for improving test adaptation practices. Journal of Applied Testing Technology,1(1), 1-30.
  • Hawi, N. (2010). Causal attributions of success and failure made by undergraduate students in an introductory-level computer programming course. Computers & Education 54, 1127–1136, Doi: 10.1016/j.compedu.2009.10.020.
  • Hernane, B., P., Gilney F., Z., & Marcelo A., M.(2010). Learning computer programming: Implementing a fractal in a Turing machine. Computers & Education 55 767-776 Doi: 10.1016/j.compedu.2010.03.009
  • Humphrey, W.S. (1999). Introduction to the team software process. Reading, MA: Addison- Wesley/Longman.
  • Kline, P. (1994). An easy guide to factor analysis. London and New York: Routledge.
  • Koohang, A. A., (1989). A Study of Attitudes Toward Computers: Anxiety, Confidence, Liking, and Perception of Usefulness. Journal of Research on Computing in Education, 20, 137-151.
  • Korkmaz, Ö. (2011). Study of validity and reliability of self-efficacy scale of teaching material utilization. Educational Research and Review (ERR). 6(15),843-853. Doi: 10.5897/ERR11.174
  • Korkmaz, Ö. (2013). Students’ Difficulties in and Opinions about Designing Algorithms According to Different Instructional Applications. Energy Education Science and Technology Part B: Social and Educational Studies , 5(1), 209-218.
  • Lau, W.W. F., & Yuen, A.H.K (2009). Exploring the effects of gender and learning styles on computer programming performance: implications for programming pedagogy. British Journal of Educational Technology, 40(4), 696–712. Doi:10.1111/j.1467-8535.2008.00847.x
  • Lee, J., & Cheng, Y.C. (2011). Change the face of software engineering education: A field report from Taiwan. Information and Software Technology 53, 51–57.
  • Levine, T., & Donitsa-Schmidt, S. (1998). Computer use, confidence, attitudes, and knowledge: a causal analysis. Computers in Human Behavior, 14, 125–146.
  • Mcdowell, C.E., Werner, L., Bullocki H. E., & Fernald, J. (2003). The Impact of Pair Programming on Student Performance and Pursuit of Computer Science Related Majors. International Conference on Software Engineering, Portland, Oregon. Available at: http://wenku.baidu.com/view/09432408f78a6529647d53be.html
  • Milne, I., & G. Rowe. (2002). Difficulties in Learning and Teaching Programming—Views of Students and Tutors. Education and Information Technologies, 7(1), 55-66.
  • Nikula, U., Gotel, O., & Kasurinen J. (2011). A Motivation Guided Holistic Rehabilitation of the First Programming. ACM Transactions on Computing Education (TOCE), 11(4). Doi: /10.1145/2048931.2048935.
  • Nilsen H., & Larsen A. (2011) Using the personalized system of instruction in an introductory programming course, NOKOBIT, 27-38. November 21-23.
  • Pioro, B. T. (2004). Performance in an introductory computer programming course as a predictor of future success for engineering and computer science majors. International Conference on Engineering Education, Gainesville, FL.
  • 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.
  • Robins, A (2010). Learning edge momentum: A new account of outcomes in CS1. Computer Science Education, 20( 1), 37–71.
  • Robins, A. Rountree, J., & Rountree, N.(2003). Learning and Teaching Programming: A Review and Discussion. Computer Science Education, 13( 2), 137–172.
  • Sacks, C., Bellisimo, Y., & Mergendoller, J., (1993). Attitudes toward computers and computer use: the issue of gender. Journal of Research on Computing in Education, 26, 257-269.
  • Scherer, R. F., Wiebe F. A., Luther, D. C., & Adams J. S. (1988). Dimensionality of Coping: Factor Stability Using the Ways of Coping Questionnaire, Psychological Reports 62(3), 763-770. PubMed PMID: 3406294.
  • Sivasakthi, M. & Rajendran, R. (2012). Learning Difficulties of OOP Paradigm Using Java: Students’ Perspective, Indian Journal of Science and Technology, 4 (8), 983-985.
  • Tan, P.H., Ting C.Y., & Ling, S.W. (2009). Learning Difficulties in Programming Courses: Undergraduates’ Perspective and Perception. International Conference on Computer Technology and Development, 42-46. DOI: 10.1109/ICCTD.2009.188
  • Winslow, L., E. (1996). Programming pedagogy–A psychological overview. SIGCSE Bulletin, 28, 17–22.
Year 2014, Volume: 1 Issue: 1, 20 - 31, 01.06.2014
https://doi.org/10.17275/per.14.02.1.1

Abstract

References

  • Anastasiadou, S.D., & Karakos, A.S. (2011). The beliefs of electrical and computer engineering students’ regarding computer programming. The International Journal of Technology, Knowledge and Society, 7(1):37-51.
  • Askar, P., &, Davenport, D. (2009). .An investigation of factors related to self-efficacy for java Programming among engineering students .The Turkish Online Journal of Educational Technology TOJET. 8(1): 26-32
  • Austin, H.S. (1987). Predictors of Pascal Programming Achievement for Community College Students. Proceedings of the eighteenth SIGCSE technical symposium on Computer science education, Missouri, United States, 161-164.
  • Bandura, A. (1986), Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice Hall, Englewood Cliffs, NJ.
  • Bergin, S., & Reilly, R. (2005). The influence of motivation and comfort-level on learning to program. In University of Sussex, Brighton, UK.
  • Boehm, B. W. (1981). Software engineering economics. Englewood Cliffs, NJ: Prentice-Hall.
  • Brooks, F. P. (1995). The mythical man-month: Essays on software engineering anniversary edition. Reading, MA: Addison-Wesley.
  • Büyüköztürk, Ş. (2002). Sosyal bilimler için veri analizi el kitabı [Data analysis handbook for social sciences]. Ankara: PegemA Press.
  • Caspersen, M.E., & Kolling M. (2009). STREAM: A first programming process. ACT Transaction on Computing Education, 9,1-29.
  • Deniz, K.Z. (2007). The adaptation of psychological scales. Ankara University, Journal of Faculty of Educational Sciences, 40(1), 1-16.
  • Dreyfus, H., & Dreyfus, S. (1986). Mind over machine: The power of human intuition and expertise in the era of the computer. New York: Free Press.
  • Erdogan, Y., Aydin, E., & Kabaca, YT. (2008). Exploring the Psychological Predictors of Programming Achievement. Journal of Instructional Psychology, 35(3), 264-270.
  • Eroglu, A. (2008). Faktör analizi [Factor Analyze]. Ş. Kalaycı (Ed.), SPSS Uygulamalı çok değişkenli istatistik teknikleri [Applied multivariate statistical technique] (ss. 321-331). Ankara: Asil Yayın Dağıtım.
  • Fang, X. (2012). Application of the participatory method to the computer fundamentals course, Affective Computing and Intelligent Interaction. Advances in Intelligent and Soft Computing, 137, 185-189.
  • Garner, S. (2009). Learning to program from Scratch. 9th IEEE Int. Conference on Advanced Learning Technologies, 451-452, Doi: 10.1109/ICALT.2009.50.
  • Gomes, A., & Mendes, A., J.(2007). Learning to program-difficulties and solutions. International Conference on Engineering Education, ICEE. 3-7 Temmuz, Coimbra, Portugal. Available at http://ineer.org/Events/ICEE2007/papers/411.pdf
  • Gülbahar, Y. & Büyüköztürk, Ş. (2008). Değerlendirme tercihleri ölçeğinin Türkçeye uyarlanması [Adaptation of evaluation choices scale into Turkish]. Hacettepe University Faculty of Education Journal, 35, 148-161.
  • Hambleton, R.K., & Patsula, L. (1999). Increasing the validity of adapted tests: myths to be avoided an guidelines for improving test adaptation practices. Journal of Applied Testing Technology,1(1), 1-30.
  • Hawi, N. (2010). Causal attributions of success and failure made by undergraduate students in an introductory-level computer programming course. Computers & Education 54, 1127–1136, Doi: 10.1016/j.compedu.2009.10.020.
  • Hernane, B., P., Gilney F., Z., & Marcelo A., M.(2010). Learning computer programming: Implementing a fractal in a Turing machine. Computers & Education 55 767-776 Doi: 10.1016/j.compedu.2010.03.009
  • Humphrey, W.S. (1999). Introduction to the team software process. Reading, MA: Addison- Wesley/Longman.
  • Kline, P. (1994). An easy guide to factor analysis. London and New York: Routledge.
  • Koohang, A. A., (1989). A Study of Attitudes Toward Computers: Anxiety, Confidence, Liking, and Perception of Usefulness. Journal of Research on Computing in Education, 20, 137-151.
  • Korkmaz, Ö. (2011). Study of validity and reliability of self-efficacy scale of teaching material utilization. Educational Research and Review (ERR). 6(15),843-853. Doi: 10.5897/ERR11.174
  • Korkmaz, Ö. (2013). Students’ Difficulties in and Opinions about Designing Algorithms According to Different Instructional Applications. Energy Education Science and Technology Part B: Social and Educational Studies , 5(1), 209-218.
  • Lau, W.W. F., & Yuen, A.H.K (2009). Exploring the effects of gender and learning styles on computer programming performance: implications for programming pedagogy. British Journal of Educational Technology, 40(4), 696–712. Doi:10.1111/j.1467-8535.2008.00847.x
  • Lee, J., & Cheng, Y.C. (2011). Change the face of software engineering education: A field report from Taiwan. Information and Software Technology 53, 51–57.
  • Levine, T., & Donitsa-Schmidt, S. (1998). Computer use, confidence, attitudes, and knowledge: a causal analysis. Computers in Human Behavior, 14, 125–146.
  • Mcdowell, C.E., Werner, L., Bullocki H. E., & Fernald, J. (2003). The Impact of Pair Programming on Student Performance and Pursuit of Computer Science Related Majors. International Conference on Software Engineering, Portland, Oregon. Available at: http://wenku.baidu.com/view/09432408f78a6529647d53be.html
  • Milne, I., & G. Rowe. (2002). Difficulties in Learning and Teaching Programming—Views of Students and Tutors. Education and Information Technologies, 7(1), 55-66.
  • Nikula, U., Gotel, O., & Kasurinen J. (2011). A Motivation Guided Holistic Rehabilitation of the First Programming. ACM Transactions on Computing Education (TOCE), 11(4). Doi: /10.1145/2048931.2048935.
  • Nilsen H., & Larsen A. (2011) Using the personalized system of instruction in an introductory programming course, NOKOBIT, 27-38. November 21-23.
  • Pioro, B. T. (2004). Performance in an introductory computer programming course as a predictor of future success for engineering and computer science majors. International Conference on Engineering Education, Gainesville, FL.
  • 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.
  • Robins, A (2010). Learning edge momentum: A new account of outcomes in CS1. Computer Science Education, 20( 1), 37–71.
  • Robins, A. Rountree, J., & Rountree, N.(2003). Learning and Teaching Programming: A Review and Discussion. Computer Science Education, 13( 2), 137–172.
  • Sacks, C., Bellisimo, Y., & Mergendoller, J., (1993). Attitudes toward computers and computer use: the issue of gender. Journal of Research on Computing in Education, 26, 257-269.
  • Scherer, R. F., Wiebe F. A., Luther, D. C., & Adams J. S. (1988). Dimensionality of Coping: Factor Stability Using the Ways of Coping Questionnaire, Psychological Reports 62(3), 763-770. PubMed PMID: 3406294.
  • Sivasakthi, M. & Rajendran, R. (2012). Learning Difficulties of OOP Paradigm Using Java: Students’ Perspective, Indian Journal of Science and Technology, 4 (8), 983-985.
  • Tan, P.H., Ting C.Y., & Ling, S.W. (2009). Learning Difficulties in Programming Courses: Undergraduates’ Perspective and Perception. International Conference on Computer Technology and Development, 42-46. DOI: 10.1109/ICCTD.2009.188
  • Winslow, L., E. (1996). Programming pedagogy–A psychological overview. SIGCSE Bulletin, 28, 17–22.
There are 41 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Research Articles
Authors

Özgen Korkmaz

Halis Altun This is me

Publication Date June 1, 2014
Acceptance Date May 14, 2014
Published in Issue Year 2014 Volume: 1 Issue: 1

Cite

APA Korkmaz, Ö., & Altun, H. (2014). Adapting Computer Programming Self-Efficacy Scale and Engineering Students’ Self-Efficacy Perceptions. Participatory Educational Research, 1(1), 20-31. https://doi.org/10.17275/per.14.02.1.1

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