Week 9 already! Wow!
This week’s Lifestream activity has been dominated by the group ‘Tweetorial’ in which we investigated some topics and issues highlighted in the recommended viewings and readings. In summarising my Tweetorial activity, I would note that I contributed to discussion threads surrounding the following key themes concerning Big Data and Learning Analytics (LA):
- Ethical considerations
- Social media influence on algorithmic culture
- Big data influence over students
- Algorithmic pattern identification
- Dependence on analytics
I felt it essential to explore the vastness of Big Data and to consider the implications of identifying patterns when it is analysed. I felt that this week’s recommended material focused on either how data was gathered/analysed or the resulting consequences for students. Therefore, I became increasingly interested in the gap between big data and hypotheses and what new knowledge we can discover from the space in between. My ‘Analyzing and modeling complex and big data’ post attempted to address this issue.
Following on from the ‘Tweetorial’ I was motivated to explore some of the issues raised to put them into a relevant context. My ‘Learning Analytics – A code of practice’ post summarised my investigation into a JISC funded LA project in which the project team addressed many (if not all) of my concerns around ethics and student intervention. In hindsight, I had only really considered LA from the perspective of the institution and the learner – not of the individual as a person.
It was another enjoyable week and I’d like to thank my tutors and peers for a very engaging Tweetorial.
Hello Stuart, thanks for this weekly summary and also for your contribution to the Tweetorial last Thursday and Friday. I’m glad you found the experience engaging: I think the success of the approach (as I experienced it) was heavily down to the quality and frequency across the group, including yourself.
‘In hindsight, I had only really considered LA from the perspective of the institution and the learner – not of the individual as a person.’
Of all the different topics we covered during the tutorial, I think this was one of the most personally interesting. I was interested to follow the conversation between yourself, Eli and others. It’s great that you’ve followed this line of interest up by looking at the JISC-funded research. Like you, I was reassured to find an ethical code of practice for institutions around the use of big data. As the conversation highlighted on Friday morning, simply because we believe we are working in the best interests of the students, we can’t assume that this will manifest in practice: after all, as you allude to, is there the danger that we damage a student’s self efficacy or motivation by designating them as being in need of ‘support’ or ‘intervention’? Another part of the same discussion on Friday suggested that we might first seek a student’s consent before using data in this way, however that does assume that a student is best placed to judge the approaches that might be educationally beneficial to them. I suppose this all goes to show that feels a bit up for grabs, which in turn highlights the value of the Tweetorial exercise and our wider reading last week.
‘Therefore, I became increasingly interested in the gap between big data and hypotheses and what new knowledge we can discover from the space in between. My ‘Analyzing and modeling complex and big data’ post attempted to address this issue.’
Again, great that you have followed up the tutorial discussion with further investigation. I actually think that of all the ideas that featured in the video lecture by Ben Williamson that we viewed last week, this is one of the most challenging and complex. Great then that you were willing to confront this head on.
Following on from your reflections this week, I’m looking forward to your blog post analysing the summary of analytics from across the Tweetorial.
Before that, I’ll look forward to catching up in one of the Google hangouts in the coming days.