A lesson with immediate relevance
One lesson I took from Weller’s (2020) discussion of the LMS in his chapter on 2002 was how well it suited the way institutions already worked. Weller notes that the LMS was often used to post notes and replicate lectures rather than support more experimental approaches to teaching, and that many institutions became stuck at that stage. The LMS became embedded in how courses were delivered without necessarily changing the teaching itself.
Cuban et al. (2001) found something similar in schools. Teachers could have good access to computers and still use them mainly to support what they were already doing in the classroom.
Generative AI makes the comparison more complicated. The technologies Cuban studied came into the classroom through schools. AI did not. Students had access before institutions had really worked out what to do with it. Teaching software development, I saw students change how they worked while many of the assignments and assessments remained the same. Cuban’s lesson still applies, but I think the issue has shifted.
With the LMS, technology adapted to the institution. With AI, students adapted first. Assessment is starting to follow, though not in one direction. The first assignment in this course asks me to declare how I used AI and to reflect on it, which is a change in how assessment is designed rather than simply adding a rule about using the technology. Watters (2020) points out that institutions have also built some of the surveillance side themselves, from plagiarism detection to online proctoring. It makes me wonder whether the response to AI will be redesign alone.
A lesson that conflicts with my experience
Weller (2020) describes connectivism, his selection for 2010, as a view of learning where knowledge is distributed across networks and the capacity to know more can matter more than what someone already knows. That runs against some of what I saw teaching software development.
Students had documentation, examples and eventually generative AI at their fingertips. They could find or generate working code very quickly. The weakness showed when something went wrong. Some students struggled to explain the code they had used or figure out why it had stopped working. That changed what I thought assessment needed to show. Producing working code was no longer enough. Students also needed to demonstrate that they understood what the code was doing and could work through a problem when it failed.
That made me question how far knowing where to find an answer can take a learner. Access to knowledge is important, but in software development you also need enough understanding to decide whether an answer makes sense and what to do when it does not.
References
Cuban, L., Kirkpatrick, H., & Peck, C. (2001). High access and low use of technologies in high school classrooms: Explaining an apparent paradox. American Educational Research Journal, 38(4), 813–834. https://doi.org/10.3102/00028312038004813
Watters, A. (2020, November 11). What happens when ed-tech forgets? Some thoughts on rehabilitating reputations. Hack Education. https://hackeducation.com/2020/11/11/forgetting
Weller, M. (2020). 25 years of ed tech. Athabasca University Press. https://www.aupress.ca/books/120290-25-years-of-ed-tech/
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