DALMOOC, episode 2: Of tools and definitions
 |
| My Twitter Analytics, 10/2014 |
Week 1 seemed to be all about on-boarding, of tools and definitions. So what is learning analytics? According to the SOLAR definition, "Learning Analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs." It's a nice, succint, definition - which I had honestly forgotten about since I was in LAK11.
Analytics has interesting potential for assisting in learning and teaching. Data collected from social interactions in the various learning spaces (the LMS comes to mind as the main one, but that's not necessarily the only one, external-to-the-LMS and internal-to-the-LMS spaces can also count as learning spaces in their own right), learning content (learner-content interaction for instance), and the data on the effects of various interventions and course changes can potentially yield useful insights.
These insights might be about the learning that might be happening, participant's patterns of interaction, their feeling and attitudes toward people and non-animate resources, how people learn, and what they might be doing next based on what they've already done (predictive analytics?). My main issue with learning analytics is that those with whom I've interacted about analytics seem to feel like this is a magic bullet, that analytics will be some sort of panacea that will help us teach better and help our learners learn. A similar thing which we've seen with the MOOC-hype mind you.
The truth is certain this cannot be quantified yet, and things that are quantified can't always tell us what's going on. As an example, I had a conversation with a colleague recently who came to me because of my background in applied linguistics and educational technology. The query was about text response length (presumably in discussion forums?) and student achievement; were there any studies around this topic? The answer (at least according to my knowledge of the field) was no, there aren't studies like that (that I know of). That said, even if someone wanted to do a study around this, I think that the study is flawed if you only look at textual comments in a discussion forum from a quantitative perspective. Length doesn't really tell you much about the quality and relevance of the posted text, other dimensions, qualitative ones, need to be examined in order to come to better conclusions (good ol' Grice comes to mind as another possible analysis dimension). Don't get me wrong, I think there probably is some positive correlation between text length in a goldilocks zone for response length, but response length isn't the end-all-be-all determinant of student achievement. If the only rubric for me getting an "A" is an essay of 4000 words, I'll just give you Lorem Ipsum text :-)
Another thing pointed out in week 1 was that there are Ethical implications and privacy issues around the use of analytics. I think that this is a much larger topic. If it comes up in a future week I'll write about it (or if you really want me to write about my thoughts on this earlier, just leave a note).
So, those were the definitions. Now for some tools! There were a number of tools discussed such as
NodeXL (free, Social Network Analysis tool), Pentaho (30 day trial, integrated suite), IBM Analytics suite (integrated suite, definitely not free), SAS (integrated suite - also not free), R Language (free), Weka (free, java based). R is something that we use in Corpus Linguistics analysis. I haven't delved too much into that field, but I am considering it since there are analytics related corpus projects that might be of interest. One of my colleagues might be teaching this course in the spring semester so I'll see if I can sit in (if I have time. Not sure how much time EDDE 802 will take). SNAPP (free) was another tool mentioned, and this is something I remember from LAK11. I've tried to get this installed on our Blackboard server over the last few years, but I've been unsuccessful at convincing the powers that be. I'd love to run SNAPP in my courses to see how connections are formed and maintained amongst the learners in my classes. This is one of the issues when you don't run your own servers, you're waiting for someone else to approve the installation of a Bb extension. Oh well... Maybe in 2015.
Anyway, those are all the tools that we won't be using directly in DALMOOC. These are the tools that we will be using: Tableau (paid, but free for us until January 2015), Gephi (free), RapidMiner (has a free version) and, LightSide (free). Gephi I already downloaded and installed because I was auditing the Coursera Social Network Analysis course that they are currently running. I'll be going back to those videos in January (or next summer, it all depends on EDDE 802) and messing around more with it then. I know we'll be using it here, but I am no sure to what extent. Tableau I already downloaded and installed last week on my work machine. I'll be messing around with Week 2 data when I get back in the office on Monday. This looks pretty interesting!
Finally (for this post anyway), DALMOOC has a bazaar assignment each week. Here is the description:
In this collaborative activity, we will reflect on what you have learned about the field of learning analytics. We would like you to do this portion of the assignment online with a partner student we will assign to you. You will use the Bazaar Collaborative Chat tool. To access the chat tool, click on the link below. You will log in using your EdX ID. When you log in, you will enter a lobby program that will assign you to a partner. If it turns out that a partner student is not available, after 5 minutes it will suggest that you try again later.
For experimentation purposes I know I should give this a try, but I probably won't do these bazaar assignments. I have an affinity for asynchronous learning (as Maha Bali put it in one of her posts) :)
SIDENOTES:
1) Great to put a face to a name. Realized that Carolyn Rose and Matt Crosslin are part of this MOOC. Carolyn is writing a piece of the upcoming special issue of CIEE (this used to be the Great Big MOOC Book), and Matt and co-authoring a piece of the special issue of CIEE journal for summer 2015 on the Instructional Design of MOOCs
2) Carolyn mentioned in one of the videos that statistics are pretty cool. I've been lukewarm on them since I was a college undergraduate, mostly because I mess up the math and my numbers don't make sense ;-)
Comments