In the course I’m taking for my MALAT, LRNT 523, Activity 6 and Assignment 3 are asking that I predict the future of Ed Tech. Since this is a broad topic, involving many disciplines and with many facets, I wanted to narrow the scope of the prediction to something I’m familiar with: the teaching of computer programming languages to post-secondary students.
So in ten years from now, in the year 2030, what will learning computer programming look like? Artificial Intelligence has advanced significantly in the last 20 years and has begun to be incorporated into teaching (Luckin & Cukurova, 2019). Computer programming has is also being introduced earlier and earlier in schools, even as early as primary schools and Kindergarten (Kazakoff & Bers, 2012; Moreno-León & Robles, 2015). While some predict, that computers will over take our teaching roles, making teachers obsolete (Selwyn et al., 2020), I prefer a more realistic approach (King et al., 2016). I see the toolsets and andragogies improving, in order to assist in teaching, however, still requiring manual intervention, and not obscuring the need for human interaction. Teachers and tools can co-exist harmoniously.
For this assignment, I will be focusing on the creation, integration, and adoption of automation tools such as automatic assignment generation, code plagiarism checkers, and automatic code marking (CodeGrade BV, n.d.).
Also, I would like to explore the following andragogies that align well with the automation tools mentioned earlier: mastery learning and adaptive learning.
Tools for teaching computer programming are a huge interest of mine – they combine two of my passions (teaching and computer programming). And even if, the prevalence of these tools and teaching methodologies do not become widespread by 2030, I plan on creating and incorporating some of them into my classroom.
CodeGrade BV. (n.d.). CodeGrade – Analytics on code assignments. Retrieved Oct 20, 2020, from https://www.codegrade.com/
Kazakoff, E., & Bers, M. (2012). Programming in a robotics context in the kindergarten classroom: The impact on sequencing skills. Journal of Educational Multimedia and Hypermedia, 21(4), 371-391.
King, M., Rothberg, S., Dawson, R., & Batmaz, F. (2016). Bridging the edtech evidence gap: A realist evaluation framework refined for complex technology initiatives. Journal of Systems and Information Technology, 18.
Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824-2838. https://doi.org/10.1111/bjet.12861
Moreno-León, J., & Robles, G. (2015, March). Computer programming as an educational tool in the English classroom a preliminary study. 2015 IEEE Global Engineering Education Conference (EDUCON), Tallinn, Estonia.
Selwyn, N., Pangrazio, L., Nemorin, S., & Perrotta, C. (2020). What might the school of 2030 be like? An exercise in social science fiction. Learning, Media and Technology, 45(1), 90-106. https://doi.org/10.1080/17439884.2020.1694944