Current Status and Future Perspectives in Articles about Coding Learning at Pre-University Level Published from 2009 to 2017: A Content Analysis Study

Year 2019, , 567 - 587, 13.12.2019

Hacer Özyurt , Özcan Özyurt , Sefa Aras

https://doi.org/10.16949/turkbilmat.477216

Abstract

In recent years,
remarkable attention has been paid to coding and computer programming learning
and teaching at pre-university level. Many countries are taking important steps
in this field every passing year. Examining the studies on coding learning enriched
by use of different tools based on specific periods may be guiding for
researchers in this field, in which there is an ever-growing interest. In this
regard, the purpose of this study is to make a thorough examination of the
articles on coding learning and teaching at pre-university level published in
international journals from 2009 to 2017 and to show the general trend. The
study examined 34 articles published in this field from 2009 to 2017 through
content analysis. The data collected by use of Article Classification Form were
descriptively analyzed. The research findings show that most articles published
in this field covered robotic tools at elementary education level. They also
indicate that quantitative – quasi-experimental method was the most preferred
method, and various data collection tools, achievements tests being in the
first place, were employed. The majority of studies reached positive results
from different perspectives while few studies reported neutral results. The
results of the present study are considered to be revealing the current
situation in this field and lighting the way for other studies to be carried
out in the future.

Keywords

Coding training, programming learning, content analysis

References

• Akpınar, Y., & Altun, A. (2014). The need for programming education in information society schools. Elementary Education Online, 13(1), 1-4.
• Alper, A., & Gülbahar, Y. (2009). Trends and issues in educational technologies: A review of recent research in TOJET. The Turkish Online Journal of Educational Technology, 8(2), 124-135.
• Altun, A., & Mazman, S.G. (2012). Validity and reliability study of Turkish form of perception of self-efficacy perception related to programming. Journal of Measurement and Evaluation in Education and Psychology, 3(2), 297- 308
• 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, 8(1), 26-32.
• Başer, M. (2013). Developing attitude scale toward computer programming. International Journal of Social Science, 6(6), 199-215.
• Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20-23.
• Bauer, M. W. (2000). Classical content analysis: A review. In M.W. Bauer & G. Gaskell (Eds.), Qualitative researching with text, image and sound (pp. 131-151). London: Sage.
• Bers, M. I., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72(2014), 145–157.
• Cohen, L., Manion, L., & Morrison, (2000). Research Methods in Education (5th ed.). London: Routledge Falmer.
• Çatlak, Ş., Tekdal, M., & Baz, F. Ç. (2015). The status of teaching programming with scratch: a document review work. Journal of Instructional Technologies & Teacher Education, 4(3), 13-25.
• Denner, J., Werner, L., & Ortiz, E. (2012). Computer games created by middle school girls: Can they be used to measure understanding of computer science concepts?. Computers & Education, 58(1), 240-249.
• Driscoll, M. (1995). Paradigms for research in instructional systems. In Gary J. Anglin (Ed.). Instructional technology: Past, present, and future (2nd ed., pp. 322-329). Englewood, CO: Libraries. Unlimited.
• Elkin, M., Sullivan, A., & Bers, M. U. (2016). Programming with the KIBO Robotics Kit in Preschool Classrooms. Computers in the Schools, 33(3), 169-186.
• 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(2013), 87-97.
• García, P. G., & De la Rosa, F. (2016). RoBlock-Web app for programming learning. International Journal of Emerging Technologies in Learning, 11(12), 45-53.
• Goh, H., & Ali, M.B. (2014). Robotics as a tool to stem learning. International Journal for Innovation Education and Research, 2(10), 66-78.
• Göktaş, Y., Küçük, S, Aydemir M., Telli E., Arpacık, Ö., Yıldırım G., & Reisoğlu, İ. (2012). Educational technology research trends in Turkey: A content analysis of the 2000-2009 decade. Educational Sciences: Theory & Practice, 12(1), 191-196.
• Guzdial, M. (2015). Learner-centered design of computing education: Research on computing for everyone. Synthesis Lectures on Human-Centered Informatics, 8(6), 1-165.
• 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.
• Kalelioğlu, F. (2015). A new way of teaching programming skills to K-12 students: Code. org. Computers in Human Behavior, 52(2015), 200-210.
• Kazakoff, E. R., Sullivan, A., & Bers, M. U. (2013). The effect of a classroom-based intensive robotics and programming workshop on sequencing ability in early childhood. Early Childhood Education Journal, 41(4), 245-255.
• Keçeci, G., Alan, B., & Zengin, F. K. (2016). Educational computer games assisted learning coding attitude scale: Validity and reliability study. Education Sciences, 11(4), 184-194.
• Korkmaz, Ö. (2012). The Impact of critical thinking and logical-mathematical intelligence on algorithmic design skills. Journal of Educational Computing Research, 46(2), 173-193.
• Lee, I., Martin, F., & Apone, K. (2014). Integrating computational thinking across the K–8 curriculum. ACM Inroad, 5(4), 64-71.
• Lin, J. M. C., & Liu, S. F. (2012). An investigation into parent-child collaboration in learning computer programming. Journal of Educational Technology & Society, 15(1), 162-173.
• 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(2014), 51-61.
• Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The scratch programming language and environment. ACM Transactions on Computing Education (TOCE), 10(4), 1-15.
• Özmen, B., & Altun, A. (2014). Undergraduate students' experiences in programming: Difficulties and obstacles. Turkish Online Journal of Qualitative Inquiry, 5(3), 1-27.
• Özyurt, H., Özyurt Ö, & Aras S. (2016). An examination of the environments where children can learn coding, 10th International Computer & Instructional Technologies Symposium, 16-18 May 2016, Rize, Turkey.
• Papadakis, S., Kalogiannakis, M., & Zaranis, N. (2016). Developing fundamental programming concepts and computational thinking with ScratchJr in preschool education: a case study. International Journal of Mobile Learning and Organisation, 10(3), 187-202.
• Pillay, N. & Jugoo, V. R. (2005). An investigation into student characteristics affecting novice programming performance. SIGCSE Bulletin 37(4), 107-110.
• Portelance, D. J., Strawhacker, A. L., & Bers, M. U. (2016). Constructing the ScratchJr programming language in the early childhood classroom. International Journal of Technology and Design Education, 26(4), 489-504.
• Saez-Lopez, J. M., Roman-Gonzalez, M., & Vazquez-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(2016), 129-141.
• Sengupta, P., Farris, A. V., & Wright, M. (2012). From agents to continuous change via aesthetics: learning mechanics with visual agent-based computational modeling. Technology, Knowledge and Learning, 17(1-2), 23-42.
• Sullivan, A., & Bers, M. U. (2016). Robotics in the early childhood classroom: learning outcomes from an 8-week robotics curriculum in pre-kindergarten through second grade. International Journal of Technology and Design Education, 26(1), 3-20.
• Topalli, D., & Cagiltay, N. E. (2018). Improving programming skills in engineering education through problem-based game projects with Scratch. Computers & Education, 120(2018), 64-74.
• Wing J.M (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.Yükseltürk, E., & Altıok, S. (2015). Pre-Service information technologies teachers' views on computer programming teaching. Amasya Education Journal, 4(1), 50-65.