Open Access Peer-reviewed Research Article

Strengthening the coding skills of teachers in a low dropout Python MOOC

Main Article Content

Fotis Lazarinis corresponding author
Anthi Karatrantou
Christos Panagiotakopoulos
Vassilis Daloukas
Theodor Panagiotakopoulos


In this paper, we present a structured approach to developing an outreach program aimed at improving the coding abilities of pre- and in-service teachers. The paper presents the design and development decisions made using the ADDIE model. External evaluators assessed the material's quality, confirmed the estimated workload, and examined the material's relevance to the educational goals. Learners’ active participation was encouraged through multiple quizzes, and learners were assisted in their learning activities by means of practical examples. Based on the number of people who actually logged into the course, a completion rate of 70.84 percent is achieved. The paper presents and discusses the findings of an evaluation conducted with the assistance of experienced teachers and course participants.

programming skills, coding, Python, teacher professional development, MOOC completion

Article Details

How to Cite
Lazarinis, F., Karatrantou, A., Panagiotakopoulos, C., Daloukas, V., & Panagiotakopoulos, T. (2022). Strengthening the coding skills of teachers in a low dropout Python MOOC. Advances in Mobile Learning Educational Research, 2(1), 187-200.


  1. Ahamed, S. I., Brylow, D., Ge, R., Madiraju, P., Merrill, S. J., Struble, C. A., & Early, J. P. (2010). Computational thinking for the sciences: A three-day workshop for high school science teachers. Proceedings of the 41st ACM technical symposium on Computer science education (pp. 42-46). Milwaukee, Wisconsin, USA: ACM.
  2. An, S., & Lee, Y. (2014). Development of Pre-service Teacher Education Program for Computational Thinking. In M. Searson & M. Ochoa (Eds.), Proceedings of SITE 2014–Society for Information Technology & Teacher Education International Conference (pp. 2055-2059). Jacksonville, Florida, United States: Association for the Advancement of Computing in Education (AACE). Retrieved December 7, 2020.
  3. Ateeq, M., Habib, H., Umer, A., & Ul Rehman, M. (2014). C++ or Python? Which one to begin with: a learner’s perspective. In International Conference on Teaching and Learning in Computing and Engineering (LaTiCE 14). IEEE, 64-69.
  4. Barr, D., Harrison, J., & Conery, L. (2011). Computational Thinking: A Digital Age Skill for Everyone. Learning & Leading with Technology, 38(6), 20-23.
  5. Bell, T., Vahrenhold, J. (2018). CS Unplugged—How Is It Used, and Does It Work?. In: B¨ockenhauer HJ., Komm D., Unger W. (eds) Adventures Between Lower Bounds and Higher Altitudes. Lecture Notes in Computer Science, vol 11011. Springer, Cham.
  6. Branch, R. (2009). Instructional design: The ADDIE approach. Berlin, Germany: Springer-Verlag.
  7. Corradini, I., Lodi, M., Nardelli, E. (2018). An Investigation of Italian Primary School Teachers’ View on Coding and Programming. 11th International Conference on Informatics in Schools: Situation, Evolution, and Perspectives, ISSEP 2018, Oct 2018, St. Petersburg, Russia. pp.228-243.
  8. Desimone, L. M. (2009). Improving impact studies of teachers’ professional development: Toward better conceptualizations and measures. Educational Researcher, 38, 181-199.
  9. Dixson, M. (2015). Measuring student engagement in the online course: the online student engagement scale (OSE). Online Learning, 19(4), 1-15.
  10. Eriksson, T., Adawi, T., & St¨ohr, C. (2017). Time is the bottleneck: A qualitative study exploring why learners drop out of MOOCs. Journal of Computing in Higher Education, 29(1), 133-146.
  11. Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration practices: A critical relationship. Computers & Education, 59, 423-435.
  12. Google, Inc., & Gallup, Inc. (2016). Trends in the state of computer science in U.S. K-12 schools.
  13. Grandell, L., Peltom¨aki, M., Back, R. J., & Salakoski. T. (2006). Why complicate things? Introducing programming in high school using Python. In Proceedings of the 8th Australasian Conference on Computing Education, 52, 71-80.
  14. Gregori, E. B., Zhang, J., Galv´an-Fern´andez, C., & Fern´andez-Navarro, F. D. A. (2018). Learner support in MOOCs: Identifying variables linked to completion. Computers & Education, 122, 153-168.
  15. Gretter, S., & Yadav, A. (2016). Computational Thinking and Media & Information Literacy: An Integrated Approach to Teaching Twenty-First Century Skills. TechTrends, 60, 510-516.
  16. Guniˇs, J., ˇ Snajder, L., Tk´aˇcov´a, Z., & Guniˇsov´a, V. (2020). Inquiry-Based Python Programming at Secondary Schools. 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), Opatija, Croatia, 2020, 750-754.
  17. Ho, A., Chuang, I., Reich, J., Coleman, C., Whitehall, J., Northcutt, C., Williams, J., Hansen, J., Lopez, G., & Peterson, R. (2015). HarvardX and MITx: Two years of open online courses. Cambridge: HarvardX.
  18. Hodges, C., Lowenthal, P., & Grant, M. (2016). Teacher professional development in the digital age: Design considerations for MOOCs for teachers. In Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 2075-2081). Chesapeake, VA: Association for the Advancement of Computing in Education (AACE).
  19. Huin, L., Bergheaud, Y., Caron, P. A., Codina, A. & Disson, E. (2016). Measuring completion and dropout in MOOCs: A learner-centered model, Proceedings of the European MOOC Stakeholder Summit 2016, 55-67.
  20. ISTE. (2014). Operational definition of Computational Thinking in K-12 education.
  21. Kellogg, S., & Edelmann, A. (2015). Massively open online course for educators (MOOC-Ed) networkdataset. British Journal of Educational Technology, 46(5), 977-983.
  22. Kesim, M., & Altinpulluk, H. (2015). A Theoretical Analysis of Moocs Types from a Perspective of Learning Theories. Procedia - Social and Behavioral Sciences, 186(2015), 15-19.
  23. Klimekov´a, E., & Tomcs´anyiov´a, M. (2018). Case Study on the Process of Teachers Transitioning to Teaching Programming in Python. In: Pozdniakov S., Dagien˙e V. (eds) Informatics in Schools. Fundamentals of Computer Science and Software Engineering. ISSEP 2018. Lecture Notes in Computer Science, vol 11169. Springer, Cham.
  24. Kostopoulos, G., Panagiotakopoulos, T., Kotsiantis, S., Pierrakeas, C., & Kameas, A. (2021). Interpretable Models for Early Prediction of Certification in MOOCs: A Case Study on a MOOC for Smart City Professionals, “Interpretable Models for Early Prediction of Certification in MOOCs: A Case Study on a MOOC for Smart City Professionals,” in IEEE.
  25. Krishnamurthi, S., & Fisler, K. (2019). Programming paradigms and beyond. In S. Fincher & A. Robins (Eds.), The Cambridge handbook of computing education research (pp. 377-413). Cambridge University Press.
  26. Kunkle, W. M. & Allen, R. B. (2016). The Impact of Different Teaching Approaches and Languages on Student Learning of Introductory Programming Concepts, ACM Transactions on Computing Education, January 2016 Article No. 3.
  27. Kwon, K. (2017). Novice programmer’s misconception of programming reflected on problem-solving plans. International Journal of Computer Science Education in Schools, 1(4), 14-24.
  28. Lamprou, A., Repenning, A., & Escherle, N. (2017). The Solothurn project — Bringing computer science education to primary schools in Switzerland. In Proceedings of the 2017 ACM conference on innovation and technology in computer science education (ITiCSE 17), 218-223. New York: ACM.
  29. Lazarinis, F., Karachristos, C.V., Stavropoulos, E.C., & Verykios, V. S. (2019). A blended learning course for playfully teaching programming concepts to school teachers. Education and information technologies, 24(2), 1237-1249.
  30. Lloyd, M., Chandra, V. (2020). Teaching coding and computational thinking in primary classrooms: perceptions of Australian preservice teachers. Curriculum Perspectives, 40, 189-201.
  31. Mannila, L., Peltom¨aki, M., & Salakoski, T. (2006). What about a simple language? Analyzing the difficulties in learning to program. Computer Science Education, 16(3), 211-227.
  32. Mason, S., & Rich, P. (2019). Preparing elementary school teachers to teach computing, coding, and computational thinking. Contemporary Issues in Technology and Teacher Education, 19(4), 790-824.
  33. M´esz´arosov´a, E. (2015). Is Python an Appropriate Programming Language for Teaching Programming in Secondary Schools? International Journal of Information and Communication Technologies in Education, 4(2), 5-14.
  34. Noone, M., & Mooney, A. (2018). Visual and textual programming languages: a systematic review of the literature. Journal of Computers in Education, 5, 149-174.
  35. Onah, F. O., Sinclair, J., & Boyatt, R. (2014). Dropout rates of massive open online courses: Behavioural patterns. In Proceedings of the 6th international conference on education and new learning technologies, Barcelona (EDULEARN14), 5825-5834. Spain.
  36. Panagiotakopoulos, T., Kotsiantis, S., Borotis, S., Lazarinis, F., & Kameas A. (2021). Applying Machine Learning to Predict Whether Learners Will Start a MOOC After Initial Registration. In: Maglogiannis I., Macintyre J., Iliadis L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 628. Springer, Cham.
  37. P´erez-Foguet, A., Lazzarini, B., Gin´e, R., Velo, E., Boni, A., Sierra-Casta˜ner, M., Zolezzi, G., & Trimingham, R., (2018). Promoting sustainable human development in engineering: Assessment of online courses within continuing professional development strategies. Journal of Cleaner Production, 172, 4286-4302.
  38. Poultsakis, S., Papadakis, S., Kalogiannakis, M., & Psycharis, S. (2021). The management of Digital Learning Objects of Natural Sciences and Digital Experiment Simulation Tools by teachers. Advances in Mobile Learning Educational Research, 1(2), 58-71.
  39. Rich, P. J., Browning, S. F., Perkins, M., Shoop, T., & Yoshikawa, E. (2018). Coding in K-8: International trends in teaching elementary/primary computing. TechTrends, 63, 311-329.
  40. Robins, A. V. (2019). Novice programmers and introductory programming. In S. Fincher & A. Robins (Eds.), The Cambridge handbook of computing education research (pp. 327–376). Cambridge, University Press.
  41. Sands, P., Yadav, A., & Good, J. (2018). Computational Thinking in K-12: In-service Teacher Perceptions of Computational Thinking. In: Khine M. (eds) Computational Thinking in the STEM Disciplines. Springer, Cham.
  42. Scherer, R., Siddiq, F., & Viveros, B. S. (2018). Technology and the mind: Does learning to code improve cognitive skills? In Proceedings of the Technology, Mind, & Society 2018 Conference.
  43. Scherer, R., Siddiq, F., & Viveros, B. S. (2018). Technology and the mind: Does learning to code improve cognitive skills? In Proceedings of the Technology, Mind, & Society 2018 Conference.
  44. Spradling, C., Linville, D., Rogers, M. P., & Clark, J. (2015). Are MOOCs an appropriate pedagogy for training K-12 teachers computer science concepts? Journal of Computer Science in Colleges, 30(5), 115-125.
  45. Toikkanen, T., & Leinonen, T. (2017). The Code ABC MOOC: Experiences from a coding and computational thinking MOOC for Finnish primary school teachers. In P. J. Rich & C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking (pp. 239–248). New York, NY: Springer.
  46. Tuomi, P., Multisilta, J., Saarikoski, P., & Suominen, J. (2018). Coding skills as a success factor for a society. Education and Information Technologies, 23, 419-434.
  47. Tzimopoulos, N., Provelengios, P., & Iosifidou, M. (2021). Implementation and evaluation of a remote seminar on the pedagogical use of educational robotics. Advances in Mobile Learning Educational Research, 1(2), 48-57.
  48. Vaca-C´ardenas, L. A., Bertacchini, F., Tavernise, A., Gabriele, L., Valenti, A., Olmedo, D. E., & Bilotta, E. (2015). Coding with Scratch: The design of an educational setting for Elementary pre-service teachers. 2015 international conference on Interactive Collaborative Learning (ICL), Florence, Italy (pp. 1171), IEEE.
  49. Weintrop, D., & Wilensky, U. (2017). Comparing block-based and text-based programming in high school computer science classrooms. ACM Transactions on Computing Education (TOCE), 18(1), 3.
  50. Yadav, A., Gretter, S., Good, J., & McLean T. (2017). Computational Thinking in Teacher Education. In: Rich P., Hodges C. (eds) Emerging Research, Practice, and Policy on Computational Thinking. Educational Communications and Technology: Issues and Innovations. Springer, Cham.