BibTex RIS Cite

ACE Cycle in Programming Education by Using Visualization

Year 2014, Volume: 5 Issue: 3, 274 - 303, 24.12.2014
https://doi.org/10.16949/turcomat.72987

Abstract

Students experience difficulties in learning computer programming. Researchers have conducted several studies with different perspectives to help students learn programming. The ACE cycle constructed in the context of mathematics education was adapted to programming education and called PACE cycle. The aim of the study was to test effectiveness of the PACE cycle by using both quantitative and qualitative measures. The sample of the study included 62 mechanical engineering students. The students were randomly assigned to control and experimental groups. Experimental group was instructed by using PACE cycle whereas control group was instructed by using ‘traditional instruction’. Both quantitative and qualitative data were gathered before and after the treatment. It was found that there is no significant difference between achievement and attitude scores of experimental and control group students. Nevertheless, it is hard to announce PACE cycle as ineffective depending only on this result. Qualitative data showed that students, in the control group, did not percieve their instruction as traditional one. This might show us that the definition of ‘traditional instruction’ can change depending on the context. It might be the case that students’ perception and experience related to their instruction affected the results of the study.

Key Words:    ACE cycle, programming education, PACE cycle, mixed-method design

References

  • Arnon, I., Cottrill, J., Dubinsky, E., Oktac, A., Fuentes, S. R., Trigueros, M., & Weller, K. (2014). APOS theory: A framework for research and curriculum development in mathematics education. Newyork/London: Springer/ Heidelberg Dordrecht.
  • Asiala, M., Brown, A., Devries, D. J., Dubinsky, E., Mathews, D., & Thomas, K. (1996). A framework for research and curriculum development in undergraduate mathematics education. In J. Kaput, A. H. Schoenfeld & E. Dubinsky (Eds.), Research in Collegiate Mathematics Education II (pp. 1-32). Providence, RI: American Mathematical Society.
  • Beth, E. W., & Piaget, J. (1966). Mathematical epistemology and psychology. Dordrecht: Reidel.
  • Cetin, I. (2013). Visualization: A tool for enhancing students’ concept images of basic object-oriented concepts. Computer Science Education, 23(1), 1-23.
  • Cetin, I. (Yayında). Students’ understanding of loops and nested loops in computer programming: An APOS theory perspective. Canadian Journal of Science, Mathematics and Technology Education.
  • Cetin, I., & Ozden, M. Y. (Değerlendirmede). Development of computer programming attitude scale.
  • Christensen, K., Rundus, D., Fujinoki, H., & Davis, D. (2002). A crash course for preparing students for a first course in computing: did it work. Journal of Engineering Education, 91(4), 409–413.
  • Creswell, J. W., & Clark, V. L. P. (2007). Designing and conducting mixed methods research. California: Sage Publications.
  • Davies, S. P. (1993). Models and theories of programming strategy. International Journal of Man-Machine Studies, 39, 237–267.
  • Dubinsky, E. (1991). Reflective abstraction in advanced mathematical thinking. In D. Tall (Ed.), Advanced Mathematical Thinking (pp. 95-126). Boston: Kluwer.
  • Dubinsky, E., Weller, K., Stringer, C., & Vidakovic, D. (2008). Infinite iterative process: the tennis ball problem. European Journal of Pure and Applied Mathematics, 1(1), 99- 121.
  • DuBoulay, B. (1986). Some difficulties of learning to program. Journal of Educational Computing Research, 2(1), 57–73.
  • Eckerdal, A., & Thune, M. (2005). Novice Java programmers’ conceptions of “object” and “class”, and variation theory. In ITiCSE ’05: Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (pp. 89–93). New York: ACM Press.
  • Fleury, A. E. (2000). Programming in java: student constructed rules. SIGCSE Bulletin, 32(1), 197–201.
  • Ginat, D. (2004). On novice loop boundaries and range conceptions. Computer Science Education, 14(3), 165-181.
  • Haberman B., & Averbuch H., (2002). The case of base cases: Why are they so difficult to recognize? – Students difficulties with recursion. SIGCSE Bulletin, 34(3), 84-88.
  • Hake, R. (1998). Interactive engagement versus traditional methods: A six-thousand student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66 (1), 64-74.
  • Hodge, B. K., & Steele, W. G. (2002). A survey of computational paradigms in undergraduate mechanical engineering education. Journal of Engineering Education, 91(4), 415–417.
  • Hundhausen, C., Douglas, S., & Stasko, J. (2002). A meta-study of algorithm visualization effectiveness. Journal of Visual Languages and Computing 13(3), 259-290.
  • Johnson, D. W., & Johnson, R. T. (1999). Learning together and alone: Cooperative, competitive, and individualistic learning (5th Ed.). Boston, MA: Allyn &Bacon.
  • Khalife, J. T. (2006). Threshold for the introduction of programming: Providing learners with a simple computer model. In Romero, P., Good, J., Acosta, E., & Bryant, S. (Eds.), Proceedings of the 18th Workshop of the Psychology of Programming Inter- est Group (pp. 244-254).
  • Mayer, R. E. (2009). Multimedia Learning. Cambridge: New York, NY.
  • Myller, N., Bednarik, R., Sutinen, E., & Ben-Ari, M. (2009). Extending the engagement taxonomy: software visualisation and collaborative learning. ACM Transactions on Computing Education, 9(1), 1-27.
  • Papanastasiou, C., & Papanastasiou, E. C. (2004). Major influences on attitudes toward science. Educational Research and Evaluation, 10(3), 239-257.
  • Pea, R. D. (1986). Language-independent conceptual “bugs” in novice programming. Journal of Educational Computing Research, 2(1), 25-36.
  • Price, B., Baecker, R. M., & Small, I. (1998). An Introduction to software visualization. In J. Visualization: Programming as a Multimedia Experience (pp. 3-27). MIT Press. Domingue, M. Brown, & B.Price (Eds.), Software
  • Shaffer, C. A., Cooper, M. L., Alon, A. J. D., Akbar, M., Stewart, M., Ponce, S., & Edwards, S. H. (2010) Algorithm visualization: The state of the field. ACM Transactions on Computing Education, 10, 1-22.
  • Slavin, R. E. (1995). Cooperative learning: Theory, research, and practice (2nd Ed.). Boston: Allyn & Bacon.
  • Sleeman, D., Putnam, R. T., Baxter, J., & Kuspa, L. (1986). Pascal and high-school students: A study of misconceptions. Journal of Educational Computing Research, 2(1), 5-23.
  • Soloway, E., Bonar, J., & Ehrlich, K. (1983). Cognitive strategies and looping constructs: an empirical study. Communications of the ACM, 26(11), 853-860.
  • Stasko, J., Kehoe, C., & Taylor, A.( 2001). Rethinking the evaluation of algorithm animations as learning aids: An observational study. International Journal of Human Computer Studies, 54(2), 265–284.
  • Sungur, S., & Tekkaya, C. (2006). Effects of problem-based learning and traditional instruction on self-regulated learning. The journal of Educational Research, 99 (5), 307- 320.
  • Urquiza-Fuentes, J., & Velázquez-Iturbide, J. Á. (2009). A Survey of successful evaluations of program visualization and algorithm animation systems. TOCE, 9(2), 1-21.
  • Winslow, L.E. (1996). Programming pedagogy - A psychological overview. SIGCSE Bulletin, 28(3), 17-22.
  • Yıldırım, A. ve Şimşek, H. (2006). Sosyal bilimlerde nitel araştırma yöntemleri. Ankara: Seçkin Yayıncılık.
  • Zyda, M. (2009). Computer science in the conceptual age. Communications of the ACM, 52(12), 66-72. Kaynak Gösterme
  • Çetin, İ. ve Top, E. (2014). Programlama eğitiminde görselleştirme ile ACE döngüsü. Türk Bilgisayar ve
  • Matematik Eğitimi Dergisi, 5(3), 274-303.
  • Citation Information
  • Çetin, İ., & Top, E. (2014). ACE cycle in programming education by using visualization. Turkish Journal of
  • Computer and Mathematics Education, 5(3), 274-303.

Programlama Eğitiminde Görselleştirme ile ACE Döngüsü

Year 2014, Volume: 5 Issue: 3, 274 - 303, 24.12.2014
https://doi.org/10.16949/turcomat.72987

Abstract

Öğrenciler bilgisayar programlamayı öğrenirken çeşitli zorluklarla karşılaşmaktadır. Araştırmacılar öğrencilere bu zorlukları aşmada yardımcı olmak için çalışmalar yapmışlardır. Bu çalışmada matematik eğitimi bağlamında geliştirilmiş olan ACE döngüsü isimli öğretim ortamı programlama eğitimine PACE döngüsü olarak uyarlanmıştır. Çalışmanın amacı PACE döngüsünün etkililiğini nicel ve nitel boyutlarıyla araştırmaktır. Çalışmanın örneklemini 62 makine mühendisliği öğrencisi oluşturmaktadır. Öğrenciler deney ve kontrol gruplarına rastgele atanmıştır. Deney grubunda PACE döngüsü kontrol grubunda ise ‘geleneksel öğretim’ uygulanmıştır. Öğretim sürecinden önce ve sonra nicel ve nitel yöntemler kullanılarak veri toplanmıştır. Elde edilen sonuçlara göre kontrol ve deney gruplarının başarı ve tutum puanları arasında anlamlı bir fark bulunamamıştır. Fakat sadece bu sonuca bağlı olarak PACE döngüsünün etkililiği yeterince değerlendirilemez. Nitel veriden elde edilen bulgular kontrol grubundaki öğrencilerin verilen öğretimi yenilikçi bir ortam olarak tanımladıklarını göstermektedir. Bu da ‘geleneksel öğretim’ ortamı tarifinin bağlamsal olarak değişebileceğine işaret eder. Öğrencilerin öğretim ortamı algısı ve deneyimleri çalışmanın sonuçlarını etkilemiş olabilir.

Anahtar Kelimeler:    ACE döngüsü, programlama eğitimi, PACE döngüsü, karma desen

References

  • Arnon, I., Cottrill, J., Dubinsky, E., Oktac, A., Fuentes, S. R., Trigueros, M., & Weller, K. (2014). APOS theory: A framework for research and curriculum development in mathematics education. Newyork/London: Springer/ Heidelberg Dordrecht.
  • Asiala, M., Brown, A., Devries, D. J., Dubinsky, E., Mathews, D., & Thomas, K. (1996). A framework for research and curriculum development in undergraduate mathematics education. In J. Kaput, A. H. Schoenfeld & E. Dubinsky (Eds.), Research in Collegiate Mathematics Education II (pp. 1-32). Providence, RI: American Mathematical Society.
  • Beth, E. W., & Piaget, J. (1966). Mathematical epistemology and psychology. Dordrecht: Reidel.
  • Cetin, I. (2013). Visualization: A tool for enhancing students’ concept images of basic object-oriented concepts. Computer Science Education, 23(1), 1-23.
  • Cetin, I. (Yayında). Students’ understanding of loops and nested loops in computer programming: An APOS theory perspective. Canadian Journal of Science, Mathematics and Technology Education.
  • Cetin, I., & Ozden, M. Y. (Değerlendirmede). Development of computer programming attitude scale.
  • Christensen, K., Rundus, D., Fujinoki, H., & Davis, D. (2002). A crash course for preparing students for a first course in computing: did it work. Journal of Engineering Education, 91(4), 409–413.
  • Creswell, J. W., & Clark, V. L. P. (2007). Designing and conducting mixed methods research. California: Sage Publications.
  • Davies, S. P. (1993). Models and theories of programming strategy. International Journal of Man-Machine Studies, 39, 237–267.
  • Dubinsky, E. (1991). Reflective abstraction in advanced mathematical thinking. In D. Tall (Ed.), Advanced Mathematical Thinking (pp. 95-126). Boston: Kluwer.
  • Dubinsky, E., Weller, K., Stringer, C., & Vidakovic, D. (2008). Infinite iterative process: the tennis ball problem. European Journal of Pure and Applied Mathematics, 1(1), 99- 121.
  • DuBoulay, B. (1986). Some difficulties of learning to program. Journal of Educational Computing Research, 2(1), 57–73.
  • Eckerdal, A., & Thune, M. (2005). Novice Java programmers’ conceptions of “object” and “class”, and variation theory. In ITiCSE ’05: Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (pp. 89–93). New York: ACM Press.
  • Fleury, A. E. (2000). Programming in java: student constructed rules. SIGCSE Bulletin, 32(1), 197–201.
  • Ginat, D. (2004). On novice loop boundaries and range conceptions. Computer Science Education, 14(3), 165-181.
  • Haberman B., & Averbuch H., (2002). The case of base cases: Why are they so difficult to recognize? – Students difficulties with recursion. SIGCSE Bulletin, 34(3), 84-88.
  • Hake, R. (1998). Interactive engagement versus traditional methods: A six-thousand student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66 (1), 64-74.
  • Hodge, B. K., & Steele, W. G. (2002). A survey of computational paradigms in undergraduate mechanical engineering education. Journal of Engineering Education, 91(4), 415–417.
  • Hundhausen, C., Douglas, S., & Stasko, J. (2002). A meta-study of algorithm visualization effectiveness. Journal of Visual Languages and Computing 13(3), 259-290.
  • Johnson, D. W., & Johnson, R. T. (1999). Learning together and alone: Cooperative, competitive, and individualistic learning (5th Ed.). Boston, MA: Allyn &Bacon.
  • Khalife, J. T. (2006). Threshold for the introduction of programming: Providing learners with a simple computer model. In Romero, P., Good, J., Acosta, E., & Bryant, S. (Eds.), Proceedings of the 18th Workshop of the Psychology of Programming Inter- est Group (pp. 244-254).
  • Mayer, R. E. (2009). Multimedia Learning. Cambridge: New York, NY.
  • Myller, N., Bednarik, R., Sutinen, E., & Ben-Ari, M. (2009). Extending the engagement taxonomy: software visualisation and collaborative learning. ACM Transactions on Computing Education, 9(1), 1-27.
  • Papanastasiou, C., & Papanastasiou, E. C. (2004). Major influences on attitudes toward science. Educational Research and Evaluation, 10(3), 239-257.
  • Pea, R. D. (1986). Language-independent conceptual “bugs” in novice programming. Journal of Educational Computing Research, 2(1), 25-36.
  • Price, B., Baecker, R. M., & Small, I. (1998). An Introduction to software visualization. In J. Visualization: Programming as a Multimedia Experience (pp. 3-27). MIT Press. Domingue, M. Brown, & B.Price (Eds.), Software
  • Shaffer, C. A., Cooper, M. L., Alon, A. J. D., Akbar, M., Stewart, M., Ponce, S., & Edwards, S. H. (2010) Algorithm visualization: The state of the field. ACM Transactions on Computing Education, 10, 1-22.
  • Slavin, R. E. (1995). Cooperative learning: Theory, research, and practice (2nd Ed.). Boston: Allyn & Bacon.
  • Sleeman, D., Putnam, R. T., Baxter, J., & Kuspa, L. (1986). Pascal and high-school students: A study of misconceptions. Journal of Educational Computing Research, 2(1), 5-23.
  • Soloway, E., Bonar, J., & Ehrlich, K. (1983). Cognitive strategies and looping constructs: an empirical study. Communications of the ACM, 26(11), 853-860.
  • Stasko, J., Kehoe, C., & Taylor, A.( 2001). Rethinking the evaluation of algorithm animations as learning aids: An observational study. International Journal of Human Computer Studies, 54(2), 265–284.
  • Sungur, S., & Tekkaya, C. (2006). Effects of problem-based learning and traditional instruction on self-regulated learning. The journal of Educational Research, 99 (5), 307- 320.
  • Urquiza-Fuentes, J., & Velázquez-Iturbide, J. Á. (2009). A Survey of successful evaluations of program visualization and algorithm animation systems. TOCE, 9(2), 1-21.
  • Winslow, L.E. (1996). Programming pedagogy - A psychological overview. SIGCSE Bulletin, 28(3), 17-22.
  • Yıldırım, A. ve Şimşek, H. (2006). Sosyal bilimlerde nitel araştırma yöntemleri. Ankara: Seçkin Yayıncılık.
  • Zyda, M. (2009). Computer science in the conceptual age. Communications of the ACM, 52(12), 66-72. Kaynak Gösterme
  • Çetin, İ. ve Top, E. (2014). Programlama eğitiminde görselleştirme ile ACE döngüsü. Türk Bilgisayar ve
  • Matematik Eğitimi Dergisi, 5(3), 274-303.
  • Citation Information
  • Çetin, İ., & Top, E. (2014). ACE cycle in programming education by using visualization. Turkish Journal of
  • Computer and Mathematics Education, 5(3), 274-303.
There are 41 citations in total.

Details

Primary Language Turkish
Journal Section Research Articles
Authors

İbrahim Çetin

Ercan Top

Publication Date December 24, 2014
Published in Issue Year 2014 Volume: 5 Issue: 3

Cite

APA Çetin, İ., & Top, E. (2014). Programlama Eğitiminde Görselleştirme ile ACE Döngüsü. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 5(3), 274-303. https://doi.org/10.16949/turcomat.72987
AMA Çetin İ, Top E. Programlama Eğitiminde Görselleştirme ile ACE Döngüsü. Turkish Journal of Computer and Mathematics Education (TURCOMAT). December 2014;5(3):274-303. doi:10.16949/turcomat.72987
Chicago Çetin, İbrahim, and Ercan Top. “Programlama Eğitiminde Görselleştirme Ile ACE Döngüsü”. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 5, no. 3 (December 2014): 274-303. https://doi.org/10.16949/turcomat.72987.
EndNote Çetin İ, Top E (December 1, 2014) Programlama Eğitiminde Görselleştirme ile ACE Döngüsü. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 5 3 274–303.
IEEE İ. Çetin and E. Top, “Programlama Eğitiminde Görselleştirme ile ACE Döngüsü”, Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 5, no. 3, pp. 274–303, 2014, doi: 10.16949/turcomat.72987.
ISNAD Çetin, İbrahim - Top, Ercan. “Programlama Eğitiminde Görselleştirme Ile ACE Döngüsü”. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 5/3 (December 2014), 274-303. https://doi.org/10.16949/turcomat.72987.
JAMA Çetin İ, Top E. Programlama Eğitiminde Görselleştirme ile ACE Döngüsü. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2014;5:274–303.
MLA Çetin, İbrahim and Ercan Top. “Programlama Eğitiminde Görselleştirme Ile ACE Döngüsü”. Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 5, no. 3, 2014, pp. 274-03, doi:10.16949/turcomat.72987.
Vancouver Çetin İ, Top E. Programlama Eğitiminde Görselleştirme ile ACE Döngüsü. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2014;5(3):274-303.