This theme was inspired by Norm Friesen’s article where he emphasizes the central role of dialogue in the imaginary of educational technology. The author describes a long history of the dialogical method of teaching and links it to the promises of personalized learning. As a student, teacher and family member, I decided to analyze how much one-to-one communication I have in my WFH life, and whether it has any educational/enlightening impact on me.
So I tracked all my one-to-one conversations for four working days (Tue-Fri) and focused on the following: 1) who I’m talking to; 2) how we do this (media); 3) how long we talk; 3) if I’m learning/developing in the process of our talk
At the start, I also tried to note down the topics we covered, but it turned out to be next to impossible to keep track of them, as they change quickly and are sometimes even difficult to define. For instance, when talking about education at work (I’m an educator) – what are we talking about ‘work’ or ‘education’? If I failed here, the speech recognition system would have done even worse. As Friesen concludes, dialogue is ‘a ubiquitous yet irreducible experience…’ that ‘cannot be reduced to the requirements and usecases of engineering nor the certainties and probabilistic measurements of the natural sciences’ (p.155).
As a result of my mini-research, I haven’t identified any ‘enlightening conversations’ in my experience this week. However, I completed training at work, read for university and reflected on new ideas in my head. I believe that without one-to-one tutoring, I did quite well as a learner this week, but if the system had tracked my dialogical activities, maybe, it wouldn’t have arrived at the same conclusion, as it tends to count ‘what is easy to be counted’ (Selwyn et al, p.534) and ‘lingers on’ the level of behavior and words.
Lovely idea to make a dataviz with Lego. I wonder if using these blocks made you think about the process of data visualization itself. You’re starting with a base board that constrains your viz, with blocks of standardized sizes and dimensions — perhaps similar to the logics of some basic dataviz software. Aim to return to hand-drawn dataviz next time and see if it changes what and how you can record and represent. But I do like your comment about the difficulty of tracking conversation topics. No doubt choices to leave out ‘noisy’ data with unclear patterns happens with other dataviz projects too. The data that are *not included* may in some respects tell us a lot about what dataviz can and cannot do or claim.
You are right, using Lego to visualize data did have some constraints in terms of size, shapes and colours – they were pre-defined, so in a way limited my creativity and freedom. On the other hand, I felt as if I delegated part of my ‘accountability’ to the Lego creators and thus, maybe, even shared the ownership of my data with them.
Sure, next time back to the hand-drawn experience and full responsibility!
I thought this was a really interesting way to visualise what you had captured, not so often you see Lego used in this way.
I have definitely noticed that it is very difficult to mark down the details. I have had ideas around what I would cover but then on day one, hour one I normally reevaluate and decide that less is more.
I wonder though how you would have noted the types of conversation with Lego. Would you have just built taller or had more blocks around each conversation?
‘I have had ideas around what I would cover but then on day one, hour one I normally reevaluate and decide that less is more.’ Same here, Collin! What you have in mind turns out to be too complicated to realize in practice. So I also tend to simplify and prioritize in the process of collecting data.
‘I wonder though how you would have noted the types of conversation with Lego’.
I’d build taller structures, since I was constrained by my the green board. In many ways, the pieces and the board defined my choices… Now it’s even more obvious to me why they encourage us to stick to hand-drawn vizs in this course.