Advertisement

Effects of different teaching approaches on programming skills

  • Ali Kürşat ErümitEmail author
Article
  • 18 Downloads

Abstract

Purpose of this study is determined effects of different teaching approaches on programming skills. Therefore, the effect of three different types of activities prepared with Scratch on 423 sixth grade students’ attitudes towards computer technologies, algorithmic thinking skills and reflective thinking skills on problem solving were investigated. Three IT teachers at the three schools, were asked to prepare and teach one of the three activities in their computer classes so that a different type of activity was provided at each school. The practical phase was carried out twice a week for seven weeks. A mixed method design was used with quantitative and qualitative components. Results showed that although the same programming tool and similar code blocks were used in the three applications, selected activities resulted in different effects on students. It was found that animation activities of the three types of activities had a positive effect on students’ attitudes towards computer technologies. The mathematical and game preparation activities had positive effects on algorithmic thinking and reflective thinking skills for problem solving. The present study reveals the importance of “activity type,” which is often neglected as a variable in studies investigating the different effects of block-based visual programming tools.

Keywords

Programming Algorithmic thinking Reflective thinking Problem solving Teaching approaches 

Notes

Compliance with ethical standards

The article is the authors’ original work and does not contain any libelous or unlawful statements or infringe on the rights or privacy of others or contain material or instructions that might cause harm or injury.

The authors have received approval by the appropriate Institutional Review Boards for all participating agencies and obtained consent and assent when appropriate. Authors agree to grant exclusive license to publish to Education and Information Technologies.

Conflict of interest

The author has no conflict of interest with any person, institution or company.

References

  1. Arnau, D., Arevalillo-Herraez, M., Puig, L., & Gonzalez-Calero, J. A. (2013). Fundamentals of the design and the operation of an intelligent tutoring system for the learning of the arithmetical and algebraic way of solving word problems. Computers & Education, 63, 119–130.  https://doi.org/10.1016/j.compedu.2012.11.020.Google Scholar
  2. Aydoğan, D. (2013). The investigation of levels of realization attainments related to 'environmental awareness' and 'information technology' in primary education curriculum. Malatya: Unpublished Doctorate Dissertation. İnönü University Education Sciences Institute.Google Scholar
  3. Baylor, A. L., & Ritchie, D. (2002). What factors facilitate teacher skill, teacher morale, and perceived student learning in technology-using classrooms? Computers & Education., 39(4), 395–414.  https://doi.org/10.1016/S0360-1315(02)00075-1.Google Scholar
  4. Bayrak, F., & Koçak Usluel, Y. (2011). The effect of blogging on reflective thinking skill. Hacettepe Universtiy Journal of Education, 40, 93–104.Google Scholar
  5. Brown, Q., Mongan, W., Kusic, D., Garbarine, E., Fromm, E., & Fontecchio A. (2013). Computer Aided Instruction as a Vehicle for Problem Solving: Scratch Programming Environment in the Middle Years Classroom. from http://www.pages.drexel.edu/~dmk25/ASEE_08.pdf. Accessed 15 June 2018.
  6. Burke, Q., & Kafai, Y. B. (2012). The writers’ workshop for youth programmers. In Proceedings of the 43 rd SIGCSE technical symposium on computer science education, (Raleigh, NC February 29–march 03) (pp. 433–438). New York, NY: ACM.Google Scholar
  7. Burton, B. A. (2010). Encouraging algorithmic thinking without a computer. Olympiads in Informatics, 4, 3–14.Google Scholar
  8. Calao, L. A., Moreno-Leon, J., Correa, H. E., & Robles, G. (2015). Developing Mathematical Thinking with Scratch. In Developing mathematical thinking with scratch an experiment with 6th grade students. Design for Teaching and Learning in a Networked World (17–27). Springer International Publishing.  https://doi.org/10.1007/978-3-319-24258-3_2.
  9. Calder, N. (2010). Using scratch: An integrated problem solving approach to mathematical thinking. Australian Primary Mathematics Classroom, 15(4), 9–14.Google Scholar
  10. Chang, C. (2014). Effects of using Alice and Scratch in an introductory programming course for corrective instruction. Journal of Educational Computing Research, 51(2), 185–204.  https://doi.org/10.2190/EC.51.2.c.MathSciNetGoogle Scholar
  11. Chang, C. K., & Biswas, G. (2011, June). Design engaging environment to foster computational thinking. In Proceedings of the world conference on educational multimedia, hypermedia and telecommunications (Vol. 1, pp. 2898–2902).Google Scholar
  12. Chen, T., Mdyunus, A., Ali, W. Z. W., & Bakar, A. (2008). Utilization of intelligent tutoring system in mathematics learning. International Journal of Education and Development using Information and Communication Technology, 4(4), 50–63.Google Scholar
  13. Choi, H. (2013). Pre-service teachers’ conceptions and reflections of computer programming using scratch: Technological and pedagogical perspectives. International Journal for Educational Media and Technology, 7(1), 15–25.Google Scholar
  14. Claypool, M. (2013). Dragonfly: Strengthening programming skills by building a game engine from scratch. Computer Science Education, 23(2), 112–137.  https://doi.org/10.1080/08993408.2013.781840.Google Scholar
  15. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: L. Erlbaum Associates.zbMATHGoogle Scholar
  16. Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods approaches (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
  17. Çetin, İ., & Dubinsky, E. (2017). Reflective abstraction in computational thinking. Journal of Mathematical Behavior., 47(2017, 70–80.  https://doi.org/10.1016/j.jmathb.2017.06.004.Google Scholar
  18. De Corte, E., & Masui, C. (2004). The CLIA-model: A framework for designing powerful leaming environments for thinking and problem solving. European Journal of Psychology of Education, 19(4), 365–384.  https://doi.org/10.1007/BF03173216.Google Scholar
  19. De Oliveria, M. G., Ciarelli, P. M., & Oliveira, E. (2013). Recommendation of programming activities by multi-label classification for a formative assessment of students. Expert Systems with Applications, 40, 6641–6651.  https://doi.org/10.1016/j.eswa.2013.06.011.Google Scholar
  20. Demo, G. B., & Williams, L. (2014). The many facets of scratch. In Y. Gülbahar & E. Karataş (Eds.), Informatics in Schools. Teaching and Learning Perspectives. ISSEP 2014. Lecture notes in computer science (Vol. 8730). Cham: Springer.Google Scholar
  21. Dewey, J. (1933). How We Think. A restatement of the relation of reflective thinking to the educative process. Boston: D. C. Heath.Google Scholar
  22. Doğan, U., & Kert, S. B. (2016). Bilgisayar Oyunu Geliştirme Sürecinin, Ortaokul Öğrencilerinin Eleştirel Düşünme Becerilerine ve Algoritma Başarılarına Etkisi. Boğaziçi Üniversitesi Eğitim Dergisi, 33(2), 21–42. ISSN 1300-9567.Google Scholar
  23. Doleck, T., Bazelais, P., Lemay, D. J., Saxena, A., & Basnet, R. B. (2017). Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: Exploring the relationship between computational thinking skills and academic performance. J. Comput. Educ., 4(4), 355–369.  https://doi.org/10.1007/s40692-017-0090-9.Google Scholar
  24. Epstein, A. S. (2009). “How planning and reflection develop young children’s thinking skills.” from http://www.journal.naeyc.org/btj/200309/Planning&Reflection.pdf. Accessed 26 Aug 2009.
  25. Erümit, A. K., Karal, H., Şahin, G., Aksoy, D. A., Gencan, A. A., & Benzer, A. İ. (2019). A model suggested for programming teaching: Programming in seven steps. Education and Science, 44(197), 155–183.  https://doi.org/10.15390/EB.2018.7678.Google Scholar
  26. Fessakis, G., & Serafeim, K. (2009). Influence of the familiarization with scratch on future teachers’ opinions and attitudes about programming and ICT in education. ACM SIGCSE Bulletin, 41(3), 258–262.  https://doi.org/10.1145/1595496.1562957.Google Scholar
  27. Fessakis, G., Gouli, E., & Mavroudi, E. (2013). Problem solving by 5–6 years old kindergarten children in a computer programming environment: A case study. Computers & Education, 63, 87–97.  https://doi.org/10.1016/j.compedu.2012.11.016.Google Scholar
  28. Futschek, G. (2006). Algorithmic thinking: The key for understanding computer science. In Mittermeir, R.T.(Ed.), ISSEP 2006, LNCS (Vol. 4226, pp. 159–168).Google Scholar
  29. Hayes, J., & Stewart, I. (2016). Comparing the effects of derived relational training and computer coding on intellectual potential in school-age children. British Journal of Educational Psychology, 86(3), 397–411.  https://doi.org/10.1111/bjep.12114.Google Scholar
  30. Hernandez, C. C., Silva, L., Segura, R. A., Schimiguel, J., Ledon, M. F. P., Bezerra, L. N. M., & Silveria, I. F. (2010). Teaching programming principles through a game engine. CLEI Electronic Journal, 13(2), 3–11.Google Scholar
  31. Hershkovitz, A., & Karni, O. (2018). Borders of chance: A holistic exploration of teaching in one-to-one computing programs. Computers & Education, 125, 429–443.  https://doi.org/10.1016/j.compedu.2018.06.026.Google Scholar
  32. Hoffman, B., & Spatariu, A. (2008). The influence of self-efficacy and metacognitive prompting on math problem solving efficiency. Contemporary Educational Psychology, 33, 875–893.  https://doi.org/10.1016/j.cedpsych.2007.07.002.Google Scholar
  33. Hromkovic, J., Kohn, T., Komm, D., & Serafini, G. (2016). Examples of algorithmic thinking in programming education. Olympiads in Informatics, 10, 111–124.  https://doi.org/10.15388/ioi.2016.08.Google Scholar
  34. Huang, T. H., Liu, Y. C., & Chang, H. C. (2012). Learning achievement in solving word-based mathematical questions through a computer-assisted learning system. Educational Technology & Society, 15(1), 248–259.Google Scholar
  35. Hwang, G. J., Chen, C. Y., Tsai, P. S., & Tsai, C. C. (2011). An expert system for improving web-based problem solving ability of students. Expert Systems with Applications, 38, 8664–8672.  https://doi.org/10.1016/j.eswa.2011.01.072.Google Scholar
  36. Israel, M., Pearson, J. N., Tapia, T., Wherfel, Q. M., & Reese, G. (2015). Supporting all learners in schoole-wide computatitonal thinking: A cross-case qualitative analysis. Computers & Education, 82(2015, 263–279.  https://doi.org/10.1016/j.compedu.2014.11.022.Google Scholar
  37. Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26.  https://doi.org/10.3102/0013189X033007014.Google Scholar
  38. Kafai, Y. B., & Burke, Q. (2015). Constructionist gaming: Understanding the benefits of making games for learning. Educational Psychologist, 50(4), 313–334.  https://doi.org/10.1080/00461520.2015.1124022.Google Scholar
  39. Kalelioğlu, F. (2015). A new way of teaching programming skills to K-12 students: Code.org. Computers in Human Behavior, 52, 200–210.  https://doi.org/10.1016/j.chb.%202015.05.047.Google Scholar
  40. Kalelioğlu, F., & Gülbahar, Y. (2014). The effects of teaching programming via scratch on problem solving skills: A discussion from learners’ perspective. Informatics in Education, 13(1), 33–50.Google Scholar
  41. Kızılkaya, G., & Aşkar, P. (2009). The development of a reflective thinking skill scale towards problem solving. Education and Science., 34(154), 82–92.Google Scholar
  42. Kobsiripat, W. (2015). Effects of the media to promote scratch programming capabilities creativity of elementary school students. Procedia-Social and Behavioral Sciences, 174, 227–232.  https://doi.org/10.1016/j.sbspro.2015.01.651.Google Scholar
  43. Kuhn, D. (1990). Developmental perspectives on teaching and learning thinking skills. New York: Jossey-Bass.Google Scholar
  44. Lai, A., & Yang, S. (2011). The learning effect of visualized programming learning on 6th graders’ problem solving and logical reasoning abilities. International Conference on Electrical and Control Engineering (ICECE), 16–18 Sept. 2011, Yichang, 6940–6944.Google Scholar
  45. Lee, Y. J. (2011). Scratch: Multimedia programming environment for young gifted learners. Gifted Child Today, 34(2), 26–31.  https://doi.org/10.1177/107621751103400208.Google Scholar
  46. Loughran, J. (1996). Developing reflective practice: Learning about teaching learning through modelling. London: Falmer Press.Google Scholar
  47. Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51–61.  https://doi.org/10.1016/j.chb.2014.09.012.Google Scholar
  48. Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). Scratch programming language and environment. ACM Transactions on Computing Education, 10(4), 1–15.  https://doi.org/10.1145/1868358.1868363.Google Scholar
  49. Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2013). Learning computer science concepts with scratch. Computer Science Education, 23(3), 239–264.  https://doi.org/10.1080/08993408.2013.832022.
  50. Milkova, E., & Hulkova, A. (2013). Algorithmic and logical thinking development: Base of programming skills. WSEAS Transactions on Computers., 2(12), 41–51.Google Scholar
  51. Mladenović, M., Rosić, M., & Mladenović, S. (2016). Comparing elementary students’ programming success based on programming environment. I.J. Modern Education and Computer Science, 8, 1–10.  https://doi.org/10.5815/ijmecs.2016.08.01.Google Scholar
  52. Mohamedi, H., Bensebaa, T., & Trigano, P. (2012). Developing adaptive intelligent tutoring system based on item response theory and metrics. International Journal of Advanced Science and Technology, 43, 1–14.Google Scholar
  53. Navarrete, C. C. (2013). Creative thinking in digital game design and development: A case study. Computers & Education, 69, 320–331.  https://doi.org/10.1016/j.compedu.2013.07.025.Google Scholar
  54. Papavlasopoulou, S., Sharma, K., & Giannakos, M. N. (2018). How do you feel about learning to code? Investigating the effect of children’s attitudes towards coding using eye-tracking. International Journal of Child-Computer Interaction, 17, 50–60.  https://doi.org/10.1016/j.ijcci.2018.01.004.Google Scholar
  55. Polya, G. (1957). How to solve it? (2nd ed.). Princeton, N.J: Princeton University Press.Google Scholar
  56. Popat, S., & Starkey, L. (2019). Learning to code or coding to learn? A systematic review. Computers & Education, 128, 365–376.  https://doi.org/10.1016/j.compedu.2018.10.005.Google Scholar
  57. Quahbi, I., Kaddari, F., Darhmaoui, H., Elachqar, A., & Lahmine, S. (2015). Learning basic programming concepts by creating games with scratch programming environment. Procedia-Social and Behavioral Sciences, 191, 1479–1482.  https://doi.org/10.1016/j.sbspro.2015.04.224.Google Scholar
  58. 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. Computer & Education, 97, 129–141.  https://doi.org/10.1016/j.compedu.2016.03.003.Google Scholar
  59. Scaffidi, C., & Chambers, C. (2012). Skill progression demonstrated by users in scratch animation environment. International Journal of Human-Computer Interaction, 28(6), 383–398.  https://doi.org/10.1080/10447318.2011.595621.Google Scholar
  60. Stanovich, K. E. (2009). Distinguishing the reflective, algorithmic, and autonomous minds: Is it time for a tri-process theory? In J. S. B. T. Evans & K. Frankish (Eds.), In two minds: Dual processes and beyond (pp. 55–88). New York, NY, US: Oxford University Press.Google Scholar
  61. Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and quantitative approaches. Applied social research methods series (Vol.46). Thousand Oaks, CA: Sage.Google Scholar
  62. Theodoraki, A., & Xinogalos, S. (2014). Studying students’ attitudes on using examples of game source code for learning programming. Informatics in Education, 13(2), 265–277.  https://doi.org/10.15388/infedu.2014.07.Google Scholar
  63. Turkey Ministry of National Education. (2017). Bilişim teknolojileri ve yazılım dersi öğretim programı, 2016–2017. Ankara: Milli Eğitim Basımevi.Google Scholar
  64. Van Merriënboer, J. J. G. (2013). Perspectives on problem solving and instruction. Computers & Education., 64, 153–160.  https://doi.org/10.1016/j.compedu.2012.11.025.Google Scholar
  65. Vicente, S., Orrantia, J., & Verschaffel, L. (2007). Influence of situational and conceptual rewording on word problem solving. British Journal of Educational Psychology, 77(4), 829–848.  https://doi.org/10.1348/000709907X178200.Google Scholar
  66. Wagner, T. (2008). The global achievement gap: Why even our best schools don't teach the new survival skills our children need and what we can do about it. Basic Books.Google Scholar
  67. Wilson, A., & Moffatt, D. C. (2010). Evaluating scratch to introduce younger schoolchildren to programming. Paper presented at 22nd annual workshop of the psychology of programming interest group. Leganes: Spain.Google Scholar
  68. Wilson, A., Hainey, T., & Connolly, T. M. (2013). Using scratch with primary school children: An evaluation of games constructed to gauge understanding of programming concepts. International Journal of Game-Based Learning, 3(1), 93–109.  https://doi.org/10.4018/ijgbl.2013010107.Google Scholar
  69. Yang, Y.T.C., Chuang Y.C., Li, L.Y., Tseng, S.S. (2013). A blended learning environment for individualized English listening and speaking integrating critical thinking. Computers & Education, 63, 285–305.  https://doi.org/10.1016/j.compedu.2012.12.012.
  70. Year, R., & Martinez, L. (2017). A recommendation approach for programming online judges supported by data preprocessing techniques. Applied Intelligence, 47(2), 277–290.  https://doi.org/10.1007/s10489-016-0892-x.Google Scholar
  71. Yen, J. C., & Chen, M. P. (2008). Patterns of reflection for problem solving in a mobile learning environment. International Journal of Education and Information Technologies, 2(2), 121–124.Google Scholar
  72. Yildiz Durak, H. (2018). The effects of using different tools in programming teaching of secondary school students on engagement, computational thinking and reflective thinking skills for problem solving. Technology Knowledge and Learning., 2018, 1–17.  https://doi.org/10.1007/s10758-018-9391-y.Google Scholar
  73. Yükseltürk, E., & Altıok, S. (2015). Pre-service information technologies teachers’ view on computer programming teaching. Amasya Education Journal, 4(1), 50–65.Google Scholar
  74. Zavala, L. A., Gallardo, S. C., & García-Ruíz, M. Á. (2013). Designing interactive activities within scratch 2.0 for improving abilities to identify numerical sequences. New York: IDC.Google Scholar
  75. Ziatdinov, R., & Musa, S. (2012). Rapid MentalСomputation system as a tool for algorithmic thinking of elementary school students development. European Researcher, 25(7), 1105–1110.Google Scholar
  76. Zsako, L., & Szlavi, P. (2012). ICT competences: Algorithmic thinking. Acta Didactica Napocensia, 5(2), 49–58 ISSN: 2065-1430.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer and Instructional TechnologyTrabzon UniversityTrabzonTurkey

Personalised recommendations