I started this week still stuck on how algorithms worked and how they might be seen to influence education. Which lead me to send my first tweet out about asking whether database results could shape research. I tweeted my question and it was in this vein of thought I went looking for any academic papers that could support what I suspected. There were a lot about bias but I found an example which I saved on Diigo. This article focused on some of the issues around systematic reviews with regards to database searches. It prompted my thinking on how research could be adversely affected by search results but more importantly highlighted the human element of how important information literacy is for scholarly processes.
It was only during the tweetathon I finally felt like I had joined the party with regards to how data and learning analytics play a role in shaping education, but it was quite difficult making sense of what was going on. I felt I was more active than I demonstrated.
I pinned a graphic from Pinterest promising that data mining and learning analytics enhance education which was reminiscent of the instrumentalism around discourse (Bayne 2014) in Block 1.
The TED talk presented how big data can be used to build better schools and transform education by showing governments where to spend their money in education. It made me realise that, when looked at quite broadly, data can revolutionise education.
Finally, I reflected on the traffic light systems that track and rate students, something I’d like to explore further. Ironically, on the first day of week ten, while I was playing catch up in Week 9, I attended some staff training on Learning Analytics, ‘Utilizing Moodle logs and recorded interactions’, where I was shown how to analyse quantitative data to monitor students’ use and engagement.
Bayne, S. (2014). What’s the matter with ‘Technology Enhanced Learning’? Learning, Media and Technology, 40(1): pp5-20.
