Thanks, Helen! I thought the use of 1812 was appropriate and representative of the nature of our Tweetorial. I tried to ‘pack in’ our two day experience into one minute!
Anne
Here’s the YouTube video of Tchaikovsky’s 1812 Overture (Finale):
from Comments for Anne’s EDC blog http://ift.tt/2nLIv88
via IFTTT
Week 11! It’s hard to believe we are in the second last week of EDC. During this week, I tried – without much luck – to integrate Evernote into my lifestream blog (see post here). I also spent some time posting about the music editing process for our final assignment HERE, and was finally able to post both the original and edited version of our music choice. Linzi and I have been contributing notes to a shared Google doc for brainstorming/planning purposes for our final assignment. We’ve also had a few lengthy Skype conversations about our collaboration where we’ve come up with some interesting and exciting ideas. I’ve also participated on Twitter again this week and have continued to add thoughts and ideas about learning analytics and data tracking and practices in education in a post found HERE. In my post, I discussed an article by Audrey Watters: How data and analytics can improve education (July 25, 2011). Watters interviewed George Siemens about the “possibilities and challenges for data, teaching, and learning.” I tried to relate the information provided in this article to my experience teaching marketing in higher education – to tracking students via learning management systems. I suggested that the analytical data gathered by LMSs gives us (teachers) ‘warning signs’ for students who, perhaps, are falling behind. I also observed that quantitative data alone cannot give us a clear picture of a student’s progress or of their actual acquisition of learning.
Finally, I created a video artefact in honour of our Tweetorial event using Tchaikovsky’s 1812 Overture (Finale) – please find it posted it HERE and tweeted it HERE.
As we approach the final week (12) in EDC, I have also been cleaning up my blog and fixing/editing/adding more information that I neglected to post earlier.
References
Watters, A. (2011, July 24). How data and analytics can improve education. Retrieved from https://www.oreilly.com/ideas/education-data-analytics-learning
For some reason I’ve had much difficulty trying to share the edited version of the music for our collaborative video. Perhaps sharing it here via Evernote will work?
As a figure skating coach and choreographer, I need to have the ability to digitally edit music for my skaters. Turns out this is a good skill to have since it’s come in handy here for our final assignment in #mscedc!
via Instagram http://ift.tt/2ou0yji
Here is the edited version:
And the original version here (via YouTube):
#mscedc I made a quick video artefact in honour of our tweetorial 😀 https://t.co/E0Wkja7PxA
I discovered this article by Audrey Watters: How data and analytics can improve education (July 25, 2011) where Watters interviews George Siemens about the “possibilities and challenges for data, teaching, and learning.” Siemens points to the use of LMSs in higher education and of how social networks (like Twitter, etc.) can be used in the same way but could raise issues of privacy since they are public platforms (Watters 2011).
Siemens also points to the significant amount data LMSs like Desire2Learn can capture (Watters 2011). When I taught at Durham College this past fall, I discovered just how much data is available to teachers using this LMS. Prior to becoming a teacher (in higher ed.), I had only experienced LMSs from a student’s perspective; it was interesting to see it from a teacher’s point-of-view. For instance, I was intrigued to see how Desire2Learn (rebranded as ‘DC Connect’ at my College) provided me with information about whether or not a student had opened content I posted on the site. I leveraged this information to track my students’ progress during study units. Although this information was not in-depth, meaning all it revealed was if my students had opened the content, therefore pointing to possible engagement with the content, it did not tell me if they had actually read the content (much more important to learning).
So, is this information useful? I posit that it did help…a little…because I used this data as a sort of warning sign; I could identify students who were falling behind by seeing that they had not opened any content for four weeks, for example. In some cases, however, this was not always accurate as I had a few students who did not often open the content I posted but, in fact, did very well in the course. Knox (2014) reminds us that learning analytics in education “makes visible the invisible” and that we can interpret the results as we wish. Did these students work with their friends and get the necessary content from their peers? Did they share the content with each other via social networks? It is hard to know what happened in all cases. I must admit, when I first investigated the data provided on the LMS my reaction when seeing that students had not opened content was that they were not engaging. After talking to a few students, I was able to gather more qualitative data which led me to change my mind about some (some had extenuating circumstances, job commitments, etc.). I suppose the take-away here is that communication is key!
Siemens, quoted in Watters (2011) suggests that “authentic” interactions occur more often over social networks and not as often on LMSs because student participation is “purposeful” there. Siemens, in Watters (2011) also mentions the Hawthorne effect; I found this idea fascinating because I can relate this ‘effect’ to my own lifestream blog. I do feel as though I modify my behaviour here on the blog because I am “aware of being observed”. I have struggled with this lifestream blog and have (sometimes) felt paralyzed to post things because I am afraid of making mistakes or of not sounding ‘scholarly’ enough. Perhaps my students at Durham College felt the same way?
Siemens (in Watters, 2011) asks this important question:
“How much should learners know about the data being collected and analyzed?”
And here is his answer:
“I believe that learners should have access to the same dashboard for analytics that educators and institutions see. Analytics can be a powerful tool in learner motivation — how do I compare to others in this class? How am I doing against the progress goals that I set? If data and analytics are going to be used for decision making in teaching and learning, then we need to have important conversations about who sees what and what are the power structures created by the rules we impose on data and analytics access” (Siemens quoted in Watters 2011).
In my own case, I would find it motivating to be able to see U of Edinburgh Moodle analytics about my work, but in the case of my students at Durham College, I found this was not a source of motivation (for most). At many points throughout the semester, I informed my class that I could see who was viewing content and who wasn’t; this may sound like a threat (which I did not intend it to be) but as a gentle reminder that I am watching and paying attention to what they are doing (or not doing). As a new teacher (in higher ed), I know I have a lot to learn about learning analytics and about how they relate to student motivation.
Watters, A. (2011, July 24). How data and analytics can improve education. Retrieved from https://www.oreilly.com/ideas/education-data-analytics-learning
@nigelchpainting @Tauraco OMG this spam is getting really strange! Too funny 😂 “reinforcing awareness never does any damage” ??? #mscedc