Examination of Undergraduate and Associate Degree Students’ Computer Programming Attitude and Self-Efficacy According to Thinking Style, Gender and Experience

Yıl 2018, Cilt: 9 Sayı: 4, 354 - 373, 16.10.2018

Mustafa Serkan Gunbatar

https://doi.org/10.30935/cet.471004

Öz

This study aimed to determine undergraduate
and associate degree students’ computer programming attitude and self-efficacy
levels, and compare them according to thinking style, gender, department,
weekly study time, and programming experience variables. The study employed the
correlational research model. The researcher attempted to reach all associate
and undergraduate students who had received the computer programming course at
a state university. The computer programming self-efficacy scale, the computer
programming attitude scale, and the holistic and analytic thinking in
problem-solving scale were used to collect research data. Results suggested
that the participants with different thinking styles showed significant
differences regarding programming attitude and programming self-efficacy. Programming
attitude and thinking style were significant predictors of programming
self-efficacy. No difference was observed between genders in terms of the
common effect and the partial effect of programming attitude and programming
self-efficacy. However; differences were observed between participants from
different departments and with different weekly study time. There was also a significant
difference between the participants with different programming experience
levels in terms of the common effect of programming attitude and self-efficacy,
whereas no difference was found in terms of attitude alone. 

Anahtar Kelimeler

Computer education, Programming self-efficay, Programming attitude, Thinking style, University students

Kaynakça

• Adamopoulos, F. (2017). An influence model of the experience of learning programming. (Unpublished doctoral dissertation). RMIT, Melbourne, Australia.
• Alsancak-Sarikaya, D. (2017, May). The effect of teaching programming on computational thinking skills. 11th International Computer & Instructional Technologies Symposium, Malatya, Turkey, 24-26 May 2017.
• Altun, A. & Mazman, S. G. (2012). Programlamaya iliskin oz yeterlilik algisi olceginin Turkce formunun gecerlilik ve guvenirlik calismasi. Journal of Measurement and Evaluation in Education and Psychology, 3(2), 297-308.
• Altun, A. & Mazman, S. G. (2015). Identifying latent patterns in undergraduate Students’ programming profiles. Smart Learning Environments, 2(1), 1.
• 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, 8(1), 26-32.
• Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman.
• Barut, E., Tugtekin, U. & Kuzu, A. (2016, October). Programlama egitiminin bilgi islemsel dusunme becerileri baglaminda incelenmesi. 4th International Instructional Technologies & Teacher Education Symposium. Elazig, Turkey, 6-8 October2016.
• Baser, M. (2013). Developing attitude scale toward computer programming. The Journal of Academic Social Science Studies, 6(6), 199-215.
• Bergin, S. & Reilly, R. (2005, February). Programming: factors that influence success. In ACM SIGCSE Bulletin, 37(1), 411-415).
• Byrne, P. & Lyons, G. (2001, June). The effect of student attributes on success in programming. ACM SIGCSE Bulletin, 33(3), 49-52).
• Cetin, I. & Ozden, M. Y. (2015). Development of computer programming attitude scale for university students. Computer Applications in Engineering Education, 23(5), 667-672.
• de Raadt, M., Hamilton, M., Lister, R., Tutty, J., Baker, B., Box, I., ... & Petre, M. (2005). Approaches to learning in computer programming students and their effect on success. In Proceedings of the 28th HERDSA Annual Conference: Higher education in a changing world (HERDSA 2005) (pp. 407-414). Higher Education Research and Development Society of Australasia.
• Erol, O. & Kurt, A. A. (2017). Investigation of CEIT students’ attitudes towards programming. Mehmet Akif Ersoy University Journal of Faculty of Education, 1(41), 314-325.
• Fong, L. L., Sidhu, G. K., & Fook, C. Y. (2014). Exploring 21st century skills among postgraduates in Malaysia. Procedia-Social and Behavioral Sciences, 123, 130-138.
• Freankel, J.R. & Wallen, N. E. (2009). How to design and evaluate research in education (7th. ed.). New York: McGraw-Hill.
• Hammouri, H. A. (2003). An investigation of undergraduates' transformational problem solving strategies: Cognitive/metacognitive processes as predictors of holistic/analytic strategies. Assessment & Evaluation in Higher Education, 28(6), 571-586.
• Hawi, N. (2010). Causal attributions of success and failure made by undergraduate students in an introductory-level computer programming course. Computers & Education, 54(4), 1127-1136.
• Hawi, N. (2014). Learning programming: a model emerging from data. International Journal of Computer Applications, 100(4), 24-34.
• Kesici, S., Sahin, I., & Akturk, A. O. (2009). Analysis of cognitive learning strategies and computer attitudes, according to college students’ gender and locus of control. Computers in Human Behavior, 25(2), 529-534.
• King, F. J., Goodson, L., & Rohani, F. (1998). Higher order thinking skills: Definition, teaching strategies, assessment. Publication of the Educational Services Program (now known as the Center for Advancement of Learning and Assessment). Retrieved on 20 January 2018 from www.cala.fsu.edu.
• Kock, N., Moqbel, M., Jung, Y., & Syn, T. (2018). Do older programmers perform as well as young ones? Exploring the intermediate effects of stress and programming experience. Cognition, Technology & Work, 20, 489-504.
• Korkmaz, O. & Altun, H. (2013). Engineering and CEIT students’ attitude towards learning computer programming. The Journal of Academic Social Science Studies, 6(2), 1169-1185.
• Korkmaz, O., Cakir, R., Ozden, M. Y., Oluk, A., & Sarioglu, S. (2015). Investigation of Individuals’ Computational Thinking Skills in terms of Different Variables. Ondokuz Mayıs University Journal of Faculty of Education, 34(2), 68-87.
• Korkmaz, O., Cakir, R., & Ozden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, 72, 558-569.
• Lau, W. W. & Yuen, A. H. (2011). Modelling programming performance: Beyond the influence of learner characteristics. Computers & Education, 57(1), 1202-1213.
• Lin, G. Y. (2016). Self-efficacy beliefs and their sources in undergraduate computing disciplines: An examination of gender and persistence. Journal of Educational Computing Research, 53(4). 540-561.
• Lin, S., Hung, T. C., & Lee, C. T. (2015). Revalidate forms of presence in training effectiveness mediating effect of self-efficacy. Journal of Educational Computing Research, 53(1), 32-54.
• Lishinski, A., Yadav, A., Good, J., & Enbody, R. (2016, August). Learning to program: Gender differences and interactive effects of students' motivation, goals, and self-efficacy on performance. Paper presented at the 12th Annual International ACM Conference on International Computing Education Research (ICER'16). Tacoma, WA. August 18-20, 206.
• Longi, K. (2016). Exploring factors that affect performance on introductory programming courses (Unpublished master’s thesis). University of Helsinki, Finland.
• Malik, S. I. & Coldwell-Neilson, J. (2018). Gender differences in an introductory programming course: New teaching approach, students’ learning outcomes, and perceptions. Education and Information Technologies, 23(5), 2453-2475.
• Mazman, S. G. & Altun, A. (2013). The effect of introductory to programming course on programming self-efficacy of CEIT students. Journal of Instructional Technologies & Teacher Education, 2(3). 24-29.
• Mazman, S. G. (2013). Modelıng The Influence Of Cognıtıve Based Indıvıdual Dıfferences On Programmıng Performance (Unpublished doctoral dissertation). Hacettepe University, Ankara, Turkey.
• Owolabi, J., Olanipekun, P., & Iwerima, J. (2014). Mathematics ability and anxiety, computer and programming anxieties, age and gender as determinants of achievement in basic programming. GSTF Journal on Computing (JoC), 3(4), 109.
• Ozden, M. Y. (2015). Computational thinking. Retrieved on 03 May 2017 from http://myozden. blogspot.com.tr/2015/06/computational-thinking-bilgisayarca.html.
• Ozyurt, O. (2015). An analysis on distance education computer programming students' attitudes regarding programming and their self-efficacy for programming. Turkish Online Journal of Distance Education, 16(2), 111-121.
• Pallant, J. F. (2007). SPSS survival manual: A step-by-step guide to data analysis with SPSS (3rd edition). New York: Open University Press.
• Pheeraphan, N. (2013). Enhancement of the 21st century skills for Thai higher education by integration of ICT in classroom. Procedia-Social and Behavioral Sciences, 103, 365-373.
• Ramalingam V. & Wiedenbeck, S. (1998). Development and validation of scores on computer programming self-efficacy scale and group analyses of novice programmer self-efficacy. Journal of Educational Computing Research, 19(4) 365-379.
• Rodrigo, M. M. T., Andallaza, T. C. S., Castro, F. E. V. G., Armenta, M. L. V., Dy, T. T., & Jadud, M. C. (2013). An analysis of java programming behaviors, affect, perceptions, and syntax errors among low-achieving, average, and high-achieving novice programmers. Journal of Educational Computing Research, 49(3), 293-325.
• Rubio, M. A., Romero-Zaliz, R., Mañoso, C., & Angel, P. (2015). Closing the gender gap in an introductory programming course. Computers & Education, 82, 409-420.
• Sáez-López, J. M., Román-González, M., & Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using “scratch” in five schools. Computers & Education, 97, 129-141.
• Sebetci, O., & Aksu, G. (2014). The effect of logical and analytical thinking skills on computer programing languages. Journal of Educational Sciences & Practices, 13(25), 65-83.
• Sharma, R. & Shen, H. (2018). Does education culture influence factors in learning programming: A comparative study between two universities across continents. International Journal of Learning, Teaching and Educational Research, 17(2), 1-24.
• Shaw, R. S. (2012). A study of the relationships among learning styles, participation types, and performance in programming language learning supported by online forums. Computers & Education, 58(1), 111-120.
• Soh, T. M. T., Arsad, N. M., & Osman, K. (2010). The relationship of 21st century skills on students’ attitude and perception towards physics. Procedia-Social and Behavioral Sciences, 7, 546-554.
• Sternberg, R.J. (2017). Theory of mental self-government: Thinking styles. Retrieved on 01 June 2017 from http://www.robertjsternberg.com/thinking-styles
• Su, A. Y. S., Yang, S. J. H., Hwang, W. Y., Huang, C. S. J., & Tern, M. Y. (2014). Investigating the role of computer-supported annotation in problem-solving-based teaching: An empirical study of a Scratch programming pedagogy. British Journal of Educational Technology, 45(4), 647-665.
• Tsai, M. J., Wang, C. Y., & Hsu, P. F. (2018). Developing the computer programming self-efficacy scale for computer literacy education. Journal of Educational Computing Research, 56(6), 1-16.
• Umay, A. & Ariol, S. (2011). A comparison of problem solving skills in terms of holistic and analytical thinking styles. Pamukkale University Journal of Faculty of Education, 30, 27-37.
• van Laar, E., van Deursen, A. J., van Dijk, J. A., & de Haan, J. (2017). The relation between 21st-century skills and digital skills: A systematic literature review. Computers in Human Behavior, 72, 577-588.
• Veerasamy, A. K., D’Souza, D., Lindén, R., & Laakso, M. J. (2018). The impact of prior programming knowledge on lecture attendance and final exam. Journal of Educational Computing Research, 56(2), 226-253.
• Ventura Jr, P. R. (2005). Identifying predictors of success for an objects-first CS1. Computer Science Education, 15(3), 223-243.
• Wilson, B. C. & Shrock, S. (2001, February). Contributing to success in an introductory computer science course: a study of twelve factors. ACM SIGCSE Bulletin, 33(1), 184-188).
• Yagci, M. (2016). Effect of attitudes of information technologies (IT) preservice teachers and computer programming (CP) students toward programming on their perception regarding their self-sufficiency for programming. Journal of Human Sciences, 13(1), 1418-1432.
• Yang, T. C., Chen, S. Y., & Hwang, G. J. (2015). The influences of a two-tier test strategy on student learning: A lag sequential analysis approach. Computers & Education, 82, 366-377.
• Yukselturk, E. & Altiok, S. (2017). An investigation of the effects of programming with Scratch on the preservice IT teachers’ self‐efficacy perceptions and attitudes towards computer programming. British Journal of Educational Technology, 48(3), 789-801
• Zhang, L. F. (2000). Relationship between thinking styles inventory and study process questionnaire. Personality and Individual Differences, 29(5), 841-856.
• Zhang, L. F. (2002). Thinking styles and cognitive development. The Journal of Genetic Psychology, 163(2), 179-195.