Research Article

An Investigation of The Effect of Block-Based Programming and Unplugged Coding Activities on Fifth Graders’ Computational Thinking Skills, Self-Efficacy and Academic Performance

Nihan Arslan Namli 1 * , Birsel Aybek 2
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1 Department of Computer Technologies, Dortyol Vocational School of Higher Education, Iskenderun Technical University, Turkey2 Curriculum and Instruction Department, Faculty of Education, Cukurova University, Turkey* Corresponding Author
Contemporary Educational Technology, 14(1), January 2022, ep341, https://doi.org/10.30935/cedtech/11477
Published: 03 January 2022
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ABSTRACT

This paper investigated the effect of block-based programming and unplugged coding teaching activities on fifth graders’ computational thinking skills, self-efficacy, and academic performance. The teaching activities were conducted within the scope of the “Problem-Solving and Programming” unit of the Information Technologies and Software (ITS) course. The sample consisted of 82 fifth graders of three public middle schools in the academic year of 2020-2021. Participants were recruited using random sampling. The study adopted an embedded mixed design. The quantitative stage employed a pretest-posttest randomized control group design, while the qualitative staged employed a case study. Quantitative data were collected using the Computational Thinking Self-efficacy Scale (CTSES), the International Informatics and Computational Thinking Activity Task Test (IICTATT), and a Computational Thinking Performance Test (CTPT) developed by the researcher. Qualitative data were collected using a semi-structured interview questionnaire. The quantitative data were analyzed using the Kruskal Wallis H, paired sample t-test, and ANCOVA test on the Statistical Package for Social Sciences (SPSS). The qualitative data were analyzed inductively using MAXQDA. There was no significant difference in CTSES scores between groups. Experimental 2 had higher IICTATT and CTPT scores than Experimental-1 and control groups. The qualitative findings were grouped into seven categories.
Keywords: computational thinking, computerless computer education, block-based programming, programming education, self-efficacy

CITATION (APA)

Arslan Namli, N., & Aybek, B. (2022). An Investigation of The Effect of Block-Based Programming and Unplugged Coding Activities on Fifth Graders’ Computational Thinking Skills, Self-Efficacy and Academic Performance. Contemporary Educational Technology, 14(1), ep341. https://doi.org/10.30935/cedtech/11477

REFERENCES

  1. Alpar, R. (2014). Applied statistics and validity-reliability with examples from sports, health and education sciences (3rd ed.). Detay Publishing.
  2. Apostolellis, P., Stewart, M., Frisina, C., & Kafura, D. (2014, June). RaBit escApe: A Board game for computational thinking [Paper presentation]. Interaction Design and Children Conference, Denmark. https://doi.org/10.1145/2593968.2610489
  3. Başol, G. (2013). Measurement and evaluation in education. Pegem Academy.
  4. Büyüköztürk, Ş., Çakmak, E. K., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2012). Scientific research methods. Pegem Academy
  5. Calder, N. (2010). Using Scratch: An integrated problem-solving approach to mathematical thinking. Australian Primary Mathematics Classroom, 15(4), 9-14.
  6. Çatlak, Ş., Tekdal, M., & Baz F.Ç. (2015). The state of teaching programming with Scratch software: A document review study. Journal of Instructional Technologies & Teacher Education, 4(3), 13-25.
  7. Chen, C., Haduong, P., Brennan, K., Sonnert, G., & Sadler, P. (2019). The effects of first programming language on college students’ computing attitude and achievement: A comparison of graphical and textual languages. Computer Science Education, 29(1), 23-48. https://doi.org/10.1080/08993408.2018.1547564
  8. Cohen, J. (1992). A coefficient of agreement for nominal scales. Educational and Psychologica Measurement, 20(1), 37-46 https://doi.org/10.1177/001316446002000104
  9. Creswell, J. W. (2012). Educational research planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson.
  10. Crues, R. W., Henricks, G. M., Perry, M., Bhat, S., Anderson, C. J., Shaik, N., & Angrave, L. (2018). How do gender, learning goals, and forum participation predict persistence in a computer science MOOC? ACM Transactions on Computing Education, 18(4), 1-14. https://doi.org/10.1145/3152892
  11. Cuny, J., Snyder, L., & Wing, J. M. (2010). Demystifying computational thinking for non-computer scientists. https://doi.org/10.1016/j.edurev.2017.09.003
  12. Davidson, K., Larzon, L., & Ljunggren, K. (2010). Self-efficacy in programming among STS students. Technical Reports from Computer Science Education course of Upssala University. http://www.it.uu.se/edu/course/homepage/datadidaktik/ht10/reports/Self-Efficacy.pdf
  13. Dr. Scratch. (2014). Dr. Scratch: Analyze your Scratch project here. http://drscratch.org/
  14. Driscoll, M. (2002). Web-based training. John Wiley & Sons, Inc.
  15. Esteves, M., Fonseca, B., Morgado, L., & Martins, P. (2011). Improving teaching and earning of computer programming through the use of the Second Life virtual world. British Journal of Educational Technology, 42(4), 624-637. https://doi.org/10.1111/j.1467-8535.2010.01056.x
  16. ET2020. (2016). Education and training 2020. http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:C:2009:119:0002:0010:EN:PDF
  17. Fadjo, C. L. (2012). Developing computational thinking through grounded embodied cognition (Unpublished Doctoral dissertation, Columbia Üniversitesi, Newyork, USA).
  18. 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
  19. Goel, S., & Kathuria, V. (2010). A novel approach for collaborative pair programming. Journal of Information Technology Education, 9, 183-196. https://doi.org/10.28945/1290
  20. Güneş, F. (2012). Developing students’ thinking skills. Turkology Studies, 32(32), 127-146.
  21. Hughes-Roberts, T., Brown, D., Standen, P., Desideri, L., Negrini, M., Rouame, A., Malavasi, M., Wager, G., & Hasson, C. (2019). Examining engagement and achievement in learners with individual needs through robotic‐based teaching sessions. British Journal of Educational Technology, 50(5), 2736-2750. https://doi.org/10.1111/bjet.12722
  22. Hung, Y.-C. (2012). The effect of teaching methods and learning style on learning program design in web-based education systems. Journal of Educational Computing Research, 47(4), 409-427. https://doi.org/10.2190/EC.47.4.d
  23. Institute for the Future (2011). “Future world skills 2020”, Institute for the Future, Palo Alto, CA.
  24. International Society for Technology in Education. (2016). National education technology standards for students. ISTE. www.iste.org/standards/nets-for-students
  25. Ismail, M. N., Ngah, N. A., & Umar, I. N. (2010). The effects of mind mapping with cooperative learning on programing performance, problem solving skill and metacognitive knowledge among computer science students. Journal of Educational Computing Research, 42(1), 35-61. https://doi.org/10.2190/EC.42.1.b
  26. Jegede, P. O. (2009). Predictors of java programming self-efficacy among engineering students in a Nigerian University. International Journal of Computer Science and Information Security, 4(1&2), 1-7.
  27. Jona, K., Wilensky, U., Trouille, L., Horn, M. S., Orton, K., Weintrop, D., & Beheshti, E. (2014). Embedding computational thinking in science, technology, engineering, and math (CT-STEM). Future directions in computer science education summit meeting, Orlando, FL. https://doi.org/10.1007/s10956-015-9581-5
  28. Kahn, K., Sendova, E., Sacristán, A. I., & Noss, R. (2011). Young students exploring cardinality by constructing infinite processes. Technology, Knowledge and Learning, 16(1), 3-34. https://doi.org/10.1007/s10758-011-9175-0
  29. 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.
  30. Kaminski, J. (2007). Use ADDIE to design online courses. Nursing-informatics.com. https://nursing-informatics.com/ADDIE.pdf
  31. Karaahmetoğlu, K. (2019). The effect of project-based arduino educational robot applications on students’ computer thinking skills and perceptions of basic stem skill levels (Master thesis), Amasya University Institute of Science and Technology, Amasya. https://doi.org/10.17275/per.19.8.6.2
  32. Kazakoff, E., & Bers, M. (2012). Programming in a robotics context in the kindergarten classroom: The impact on sequencing. Journal of Educational Multimedia and Hypermedia, 21(4), 371-391.
  33. Kırçalı, A. Ç. (2019). Valuation of computerized and non-computerized tools used in algorithm teaching at K12 level in terms of various variables (Unpublished master thesis). Marmara University, Istanbul.
  34. Kordaki, M. (2010). A drawing and multi-representational computer environment for beginners’ learning of programming using C: Design and pilot formative evaluation. Computers & Education, 54(1), 69-87. https://doi.org/10.1016/j.compedu.2009.07.012
  35. Kose, U., Koc, D., & Yucesoy, S. A. (2013). Design and development of a sample ‘‘computer programming’’ course tool via story-based e-learning approach. Educational Sciences in Theory and Practice, 13(2), 1235-1250.
  36. Kukul, V. (2018). The effect of different structured processes in programming teaching on students’ computational thinking skills, self-efficacy and programming success (Unpublished doctoral dissertation). Gazi University, Ankara, Turkey.
  37. Kurhila, J., & Vihavainen, A. (2015). A purposeful MOOC to alleviate insufficient CS education in Finnish schools. ACM Transactions on Computing Education, 15(2), 1-18. https://doi.org/10.1145/2716314
  38. Kyungbin, K., & Jonassen, D. H. (2011). The influence of reflective self-explanations on problem-solving performance. Journal of Educational Computing Research, 44(3), 247-263. https://doi.org/10.2190/EC.44.3.a
  39. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159-174. https://doi.org/10.2307/2529310
  40. Lin, J. M. C., & Liu, S. F. (2012). An investigation into parent-child collaboration in learning computer programming. Educational Technology & Society, 15(1), 162-173.
  41. Lockwood, J., & Mooney, A. (2017). Computational thinking in education: Where does it fit? A systematic literary review. https://arxiv.org/abs/1703.07659
  42. Malan, D. J., & Leitner, H. H. (2007). Scratch for budding computer scientists. ACM Bulletin, 39(1), 223-227. https://doi.org/10.1145/1227310.1227388
  43. Mannila, L., Dagiene, V., Demo, B., Grgurina, N., Mirolo, C., Rolandsson, L., & Settle, A. (2014). Computational thinking in K-9 education. In Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, ITiCSE-WGR 2014, 1–29. ACM, New York. https://doi.org/10.1145/2713609.2713610
  44. Maykut, P., & Morehouse, R. (1997). Beginning qualitative research: A philosophic and practical guide. Falmer Press.
  45. Mazman, S. G., & Altun, A. (2013). The effect of Programming-I course on the self-efficacy perceptions of CEIT department students about programming. Journal of Instructional Technologies and Teacher Education, 2(3), 24-29.
  46. McGriff, S. J. (2000). Instructional system design (ISD): Using the ADDIE model. https://www.lib.purdue.edu/sites/default/files/directory/butler38/ADDIE.pdf
  47. Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2010). Learning computer science concepts with scratch. In Proceedings of the Sixth International Workshop on Computing Education Research (ICER ‘10) ((pp. 69-76). New York. USA. https://doi.org/10.1145/1839594.1839607
  48. Ministry of National Education of Turkey. (2018a). Information technologies and software course curriculum.
  49. Ministry of National Education of Turkey. (2018b). Computer science course curriculum set1 - set2. http://mufredat.meb.gov.tr/ProgramDetay.aspx?PID=335
  50. Missiroli, M., Russo, D., & Ciancarini, P. (2017). Cooperative thinking, or: computational thinking meets agile. 2017 IEEE 30th Conference on Software Engineering Education and Training (CSEE&T) (pp. 187-191). GA. https://doi.org/10.1109/CSEET.2017.37
  51. Moreno-Leon, J., & Robles, G. (2012). Analyze your Scratch projects with Dr. Scratch and assess your computational thinking skills. In Scratch Conference (pp. 12-15). https://doi.org/10.1145/2818314.2818338
  52. Oluk, A., & Korkmaz, Ö. (2016). Comparing students’ Scratch skills with their computational thinking skills in terms of different variables. International Journal of Modern Education and Computer Science, 8(11), 1-7. https://doi.org/10.5815/ijmecs.2016.11.01
  53. Özcan, S. N., Ergün, K., Özge, K. & Neriman, E. 2017). High school students’ views on the use of Scratch program in computer programming education. International Journal of Scientific Research, 182-188.
  54. Özel, O. (2019). The effect of programming methods on secondary school students’ computational thinking skills, self-efficacy perception and programming success (Doctoral dissertation), Marmara University, Turkey.
  55. Patton, M. Q. (2002). Qualitative evaluation and research methods (3rd ed.). Sage Publications.
  56. Ramalingam, V., LaBelle, D., & Wiedenbeck, S. (2004, June). Self-efficacy and mental models in learning to program. In Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education (pp. 171-175). https://doi.org/10.1145/1007996.1008042
  57. Riley, D. D., & Hunt, K. A. (2014). Computational thinking for the modern problem solver. CRC Press. https://doi.org/10.1201/b16688
  58. Robertson, J. (2013). The influence of a game-making project on male and female learners’ attitudes to computing. Computer Science Education, 23(1), 58-83. https://doi.org/10.1080/08993408.2013.774155
  59. Sakamoto, K., Takano, K., Washizaki, H., & Fukazawa, Y. (2013). Learning system for computational thinking using appealing user interface with icon-based programming language on smartphones. In Proceedings of the 21st International Conference on Computers in Education, ICCE.
  60. Shin, S., & Park, P. (2014). A study on the effect affecting problem solving ability of primary students through the Scratch programming. Advanced Science and Technology Letters, 59, 117-120. https://doi.org/10.14257/astl.2014.59.27
  61. Uslu, N. A. (2018). The effect of visual programming activities on the computational thinking skills of secondary school students. Aegean Journal of Educational Technologies, 2(1), 19-31.
  62. Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: The kappa statistic. Family Medicine, 37(5), 360-363.
  63. Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). K 6 computational thinking curriculum framework-implications for teacher knowledge. Educational Technology & Society, 19(3), 47-57.
  64. Wang, L. C., & Chen, M. P. (2010). The effects of game strategy and Preference matching on flow experience and programming performance in game-based learning. Innovations in Education and Teaching International, 47(1), 39-52. https://doi.org/10.1080/14703290903525838
  65. Wang, Y., Li, H., Feng, Y., Jiang, Y., & Liu, Y. (2012). Assessment of programming language learning based on peer code review model: Implementation and experience report. Computers & Education, 59(2), 412-422. https://doi.org/10.1016/j.compedu.2012.01.007
  66. Wiedenbeck, S. (2005). Factors affecting the success of non-majors in learning to program. In Proceedings of the first international workshop on Computing education research (pp. 13-24). https://doi.org/10.1145/1089786.1089788
  67. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
  68. Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), 1-16. https://doi.org/10.1145/2576872
  69. Yünkül, E., Durak, G., Çankaya, S., & Abidin, Z. (2017). The effects of Scratch software on students’ computational thinking skills. Necatibey Education Faculty Electronic Journal of Science and Mathematics Education, 11(2), 502-517. https://doi.org/10.1007/s10758-018-9391-y
  70. Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K9. Computers & Education, 141, 103607. https://doi.org/10.1016/j.compedu.2019.103607