So. I just imported the 8 whole posts I’d published over there, into my blog here. They’re now in a separate category called, creatively enough, phdnotes. Yeah. I added a navigation link to the theme, and there’s an RSS feed just for those posts (does anyone else still do RSS?). I’ll be posting stuff there as my program starts up (officially kicks off in September) and I start to get ideas about what I’d like to work on.

My Masters degree thesis was based on the use of social network and discourse analysis in an online course to attempt to understand the differences in student activity and interactions in two different online platforms and course designs. Tools like Gephi and NodeXL are available to anyone teaching online, to feed the data (system-generated activity logs, raw discussion text, twitter hashtags, search queries etc.) and get a powerful visualization of how the students interacted. It struck me that the tools are so much richer for online interactions than they are for offline (or blended) face-to-face interactions.

As part of our work in the Taylor Institute, we work closely with instructors and students in classroom-based face-to-face courses, in support of their teaching and learning as well as their research and dissemination about what they learn while teaching (and learning) in the Institute. That is something that could definitely use visualization tools similar to Gephi and NodeXL, as ways to document and share the patterns of interactions between students in various experimental course designs and classroom activities.

There are several layers that need simultaneous documentation and analysis in a classroom, including at least:

1. Environment. The design of the learning spaces and technologies available in those spaces.
2. Performance. What people actually do while participating in the session.
3. Learning. This includes course design, instructional design, and the things that people take away from the session(s).

Environment

At the most basic level, this includes the architectural, design, and technology integration schematics. What are the dimensions of the space? Where is the “front” of the space? What kinds of furniture are in the space? How is it arranged? How can it be re-arranged by participants? How is functionality within the space controlled? Who has access to the space during the sessions? Who is able to observe?

This kind of documentation might also be informed by theatre research methods, including scenography, where participants document their interpretation of the space in various forms, and how it shaped their interactions with each other (and, by extension, their teaching and/or learning).

Performance

What do people (instructors, students, TAs, other roles) do during the session. This might involve raw documentation through video recording of the session, which might also then be post-processed to generate data for interpretation. Who is “leading” parts of the session? What is the composition of participants (groups? Solo? Large-class lecture? Other?) Who is able to present? To speak? To whom? How are participants collaborating? Are they creating content/media/art/etc? How are they doing that?

There is some existing work on this kind of documentation, but I think it gathers too much data, making it either too intrusive or too difficult to manage. Ogan & Gerritsen’s work on using Kinect sensors to record HD video and dot matrices from a session is interesting. McMasters’ LiveLab has been exploring this for awhile, but its implementation is extremely complicated and couldn’t be replicated in other spaces without significant investment, and would be difficult in a classroom setting.

This layer might also be a candidate for methods such as classroom ethnography or microethnography – both of these methods provide rich data for interpretation, but both are incredibly resource intensive, requiring much time and labour to record, analyze, code, and interpret the data. I think this is where the development of new tools – the field of computational ethnography – might come into play. What if the interactions and performances could be documented and data generated in realtime (or near realtime) through the use of computerized tools to record, process, manipulate, and interpret the raw data to generate logs akin to the system-generated activity logs used in the study of online learning?

There are likely many other research methods employed in theatre which might be useful in this context. I’m taking a research methods course in the fall semester that should help there…

Learning

Most of the evaluation of learning will be domain-specific, and within the realm of the course being taught in the classroom session. But, there may be other aspects of student learning that could be used – perhaps a subset of NSSE? Rovai’s Classroom Community Scale? Garrison, Anderson and Archer’s Community of Inquiry model?

What might this look like?

I put together some super-rough sketches of what microethnographic documentation of a classroom session might look like. I have a few ideas for how the documentation may be automated, and need to do a LOT more reading before I try building anything.

https://links.darcynorman.net/bookmarks.php/dnorman/ethnography
https://links.darcynorman.net/bookmarks.php/dnorman/microethnography

Just realized that last post about soundscape app for iOS sounded horribly pretentious. Wow. Not sure I’ll get to Thing Explainer level, but I’m going to try really hard to break out of jargon.

So. What I was meaning to say was that I’m messing around with a cool iOS app for making computer generated sounds (not music, but not noise). I want to try playing with it to see what kinds of sounds I can make with it, and am wondering about how those sounds might be triggered or changed based on signals such as people moving through a room or something else. I started playing with converting movement into signals and that kind of works. Now I need to figure out what to do with that data – what kinds of sounds or visuals might be interesting in response to what people are doing in a space.

Trying out Soundscaper as a way to explore synthetic modulated ambient soundscapes, to see what kinds of sounds might work for algorithmic generation from spatial data…

http://motion-soundscape.blogspot.ca/2015/02/soundscaper-experimental-sound-mini-lab.html

It started as a “skunkworks” project idea:

An atrium-sized theremin, so a person (or a few people, or a whole gaggle of people) could make sounds (or, hopefully, music) by moving throughout the atrium. A theremin works by responding to the electrical field in a person – normal-sized theremins respond to hand movements. An atrium-sized theremin might respond to where a person walks or stands in the atrium, or how they move. I have absolutely NO idea how to do this, but think it could be a fun way to gently nudge people to explore motion and position in a space. Bonus points for adding some form of synchronized visualization (light show? Digital image projection? Something else?)

So I started hacking stuff together to see what might work, and also to see if I could do it. I got the basic motion detection working great, using the imutils Python library. I then generated raw frequencies to approximate notes (based on the X/Y coordinates of an instance of motion).

Turn your volume WAY down. It sounds like crap and is horribly loud. But the concept worked. Motion tracking by a webcam overlooking the atrium of the Taylor Institute (the webcam was only there for the recording of this demo – it’s not a permanent installation), run through motion detection and an algorithm that calculates frequencies for notes played by each instance of movement during a cycle (the “players” count).

I updated the code after making this recording to refresh the motion detection buffer more frequently, so things like sunlight moving across a polished floor don’t trigger constant notes.

Next up: try to better explore what soundscapes could be algorithmically generated or modified in response to the motion input. Possibly using CSound?

and an updated version with improved motion detection (and annoying audio stripped out):