We started off by looking at cyberculture and the connection it has to techonology enhanced learning. The ideas of transhumanism and posthumanism definitely opened my eyes to the way I interact with technology in my day to day life as well as the dependence I have on it. These ideas have transcended in many forms of media which seem to depict that a technological singularity event could be inevitable. The first theme was actually what got me hooked on EDC. It truly captured my imagination and expanded into a divergent way of thinking. Theme one whet my appetite for more and what came next thoroughly engrossed me.

Theme two was about exploring digital cultures. Now, I am no sociologist and don’t have the first clue on how to analyse and critique on individuals in a social group. I will admit though that I have always had a keen interest in anthropology. I like to travel to different countries and learn about the culture of the people. Theme two took this idea and brought it to an online space, specifically to a MOOC. It was fascinating to learn about the different types of individuals who interact in the community and the ways in which their interaction affects the dynamic of the group. The digital ethnography study we conducted was a lot of fun and was very interesting especially when looking at the specific typologies within the communities. The activity  also acted as a reflection of my own interaction within online spaces and communities. The study did put into perspective the importance of the online communities role within online education. The study was both enjoyable and frustrating at the same time which I would have loved to spend more time on (perhaps in the final assignment).

The algorithmic cultures section was a great note to end the course on. This particular topic allowed me to test a particular algorithm and get some quantifiable results. I am a frequent user of YouTube and decided to play around with their Watch Time algorithm. The research was enjoyable and quite conclusive to the inner workings of this piece of code. Again, it felt like more could of been done with this assignment on my part and I hope to venture back to this topic in the future.

I would like to thank the course tutors Dr Jeremy Knox and Dr. Sian Bayne for their diligence and support for the participants on the course. It is clear to see that they love what they do and are passionate about teaching. They have presented an intriguing and thought provoking course which I would now definitely recommend to future students. Also a large thanks must be sent out to my colleagues, who engaged with each other and me given constant and constructive feedback throughout the course. I wish them all the best of luck for their upcoming courses and hope to see them online soon.

In the immortal words of one of the greatest Sci-Fi characters…” Live long and prosper”

Cheers

Em

P.S. This post is dedicated to Leonard Nimoy.

Looking at this graphic again also made me curious to why I did not use a mobile application in order to closely engage in the Tweetorial. I have a smart phone, ipad and smartwatch that I carry on my person all the time . All of these devices have twitter installed but I never thought to engage with the tweetorial via these mediums, rather opting to only engage after first logging into the EDC portal and then transferring over to twitter when I got home.

Figure 1: Source of Tweet graphic shows that majority of tweets derived from Twitter web client

The summaries were not always that clearly definable. I found it particular difficult to extrapolate any useful or meaningful information from them. This was a particular topic that came up during our Google Hangouts this week. Many people, including myself, felt as though the summaries did not reflect truely what was happening during the Tweetorial. For example, the tweet archivist only looks at number of tweets per person and perhaps not the quality of the individual tweets. This particular set of data is superficial in a way that perhaps only looks at the amount of time each person spends on twitter.

During the MOOC netnography assignment I came across a very interesting feature of the discussion forums.  Users would be rated based on how many upvotes it receives. Forum posts as well as forum threads were ranked by points (see figure 2) This way seems to a more realistic representation of the quality of the posts and not the just based on how many posts a particular person produces within the forum. Perhaps a specific analytic they could have included was the number of posts that received retweets. I feel that such data would be useful and serve to display the significance of a participants contribution to the tutorial.

Figure 2: A snapshot of points allocation to threads within forums

As mentioned earlier the summaries in some cases may appear somewhat superficial and less meaningful than what they could be. In terms of using these sorts of analytics in education, summaries on top hashtags, top images and top words could be most useful. These particular summaries and visualisations express certain trends and ideas as they develop within the tutorial. Top words and top hashtags are extremely useful because it helps to illustrate a theme that developed  throughout the tweetorial. This will allow tutors to pin point major topics or issues that were common amongst the participants and address these further.

So a question we were posed this week was how can we use these recommendation algorithms in education. These algorithms need to work in the same way in which videos are recommended on YouTube for the consumer. However instead of suggesting videos the algorithms would suggest courses learners could enroll in. These optimization algorithms , also termed personalization algorithms (Rauch, Andrelczyk and Kusiak, 2007), collect user information and analyze the data so that it may be relayed to the user at specific moments (Venugopal, Srinivasa and Patnaik, 2009). For example, when I am finished watching a video on YouTube or movie on Netflix I am surely presented with a list of recommendations on the genres I have just consumed. This idea works similar with personalization algorithms that would be able to recommend courses or avenues of learning based on the learners prior knowledge or courses completed.

An example of a personalized learning algorithm I came across was PERCEPOLIS (Hurson and Sedigh, 2010). This particular algorithm adapts various pieces of information and takes the individual pieces to produce a recommendation for the user. The information analyzed was data from a learner profile, teacher profile and environment profile. These are all known as learning analytics. I can definitely see the real world applications of such a tool especially when students are going into colleges blindly not knowing what they want to study. This could help alleviate some of that pressure and make the student’s learning more personalized. I know I could have used assistance like this when I was in my undergrad. Would have saved myself a lot of headaches and tears.

Searching this topic of personalized education further I found that perhaps the term personalization is misused and  what the algorithm is doing is not personalizing the learning at all. Instead the algorithm is taking the onus away from the learner. Trent Baton (2015) believes that personalization of learning is still the responsibility of the learner. He does admit though that it is impossible to deny algorithms and the importance they play in our lives but one needs to understand that technology is there to enrich or enhance human lives, not to run it. This is his biggest fear with regard to recommendation algorithms in education. I tend to agree with him. Ultimate control still needs to be with the learner and not just left to an equation. He goes on to say that, “Algorithms, like good teachers, need to be guides on the side and not new sages on the stage.” His thoughts here are very explicit. We need to maintain control of our own learning.

It was an extremely eye-opening week and on to the next one.

Em

References:

Baston, Trent. (2015). “Personalized Learning: It’s Not the Algorithm, It’s the Learner – AAEEBL.” Personalized Learning: It’s Not the Algorithm, It’s the Learner – AAEEBL. Association for Authentic, Experiential and Evidence-based Learning, 17 Feb. 2015. Web. 15 Mar. 2015. <http://www.aaeebl.org/blogpost/1008436/208980/Personalized-Learning-It-s-not-the-Algorithm-It-s-the-Learner>

Hurson, A., Sedigh, S. (2010) PERCEPOLIS: Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support. Intelligent Information Management, Vol. 2 No. 10, 2010, pp. 586-596.

Rauch, L., Andrelczyk, K., Kusiak J. (2007) Agent-based Algorithm Dedicated to Personalization of e-Learning Courses. In: 20th EADTU, Lisbon

Venugopal, K.R., Srinivasa, K.G., Patnaik, L.M. (2009). Algorithms for Web Personlisation. Studies in Computational Intelligence Volume 190, 2009, pp 217-230