Time spent preparing for a lesson and my performance in a lesson.
The data that I have collected is subjective, and many other factors need to be considered when evaluating this data and taking further actions. For instance, I gave a grade for my personal performance in each lesson. However, multiple factors could have influenced my decision. Why did I evaluate one class as a six and another one as a seven? Is there a clear distinction? I have not thought thoroughly about the meaning of a specific grade before collecting data. This needs to be improved in the future, and clear criteria for each specific grade mean need to be established.
Key finding: Does time spent preparing for a lesson influence my performance in a lesson? No
A small face represents each lesson that I had this week. Every speech bubble represents how much time did I spend preparing for a lesson. There is no clear relationship between these two factors. It seems that lesson success involves more things than just spending time getting ready for it.
Visualization design choices: faces were chosen to illustrate how I evaluated my performance. Smile illustrated high performance, while sad faces low. The colour of the lines of speech bubbles showed how much time do I spend preparing. The green colour represented that I spent a high amount of time preparing when red showed the opposite. Also, I was trying to represent my data, not as a dashboard, because it can often harm education (Brown, 2020).
This type of data might be useful for every teacher to evaluate how much time they spend preparing for lessons and whether it really influences their performance. It can reveal ‘truths’ about education (Beer 2019, cited by Williamson et al. 2020). Maybe spending their time developing other things would be more beneficial? Such as deploying new technologies and becoming ‘data literate’ (Williamson et al. 2020)?
However, it is important that such information would be collected by instructors themselves because otherwise, it can seem like surveillance (Brown, 2020). Often learning analytics intervene in education in counterproductive ways. Tools need to be aligned with practitioners’ values and views.
References
Brown, M. (2020). Seeing students at scale: How faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education, 25(4), 384-400.
Williamson, B., Bayne, S. & Shay, S., (2020). The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in higher education, 25(4), pp.351–365.
2 replies on “‘Teaching with data’”
Really interesting visualisation here Vilius.
‘However, multiple factors could have influenced my decision. Why did I evaluate one class as a six and another one as a seven? ‘
I think you are right to be cautious about the ‘subjective’ nature of the data collection here, but it is also worth recognising that automated, digital, data collection is just as contingent – for example, an algorithm might decide that a class was a seven and not a six, but there would be a human decision, and an underlying assumption about what ‘good performance is, under-pining the system. The point being that we need to see ‘subjective’ and ‘objective’ not as hard distinctions when it comes to data.
‘I was trying to represent my data, not as a dashboard, because it can often harm education (Brown, 2020).’
Interesting, and I think you’ve achieved something here which avoids stark measurement, and seems to convey ‘quality’, rather than ‘quantity’.
‘However, it is important that such information would be collected by instructors themselves because otherwise, it can seem like surveillance’
Good point here, and I think it is definitely valuable to consider how teachers might design useful data visualisations – too often, design and data collection seems to be defined by technicians, rather than teachers.
Thank you Jeremy! Great points!