Week 9 of Education and Digital Cultures was my first experience of a ‘Tweetorial’. It was a very public way for our group to explore the topic of ‘Learning Analytics and Calculating Academics’. The openness was certainly consistent with the ethos of the course as a whole. The activity encouraged the group to engage with each other (and indeed the wider Twitter community) to discuss a range of topics that were explored throughout the previous few weeks. The benefit of using Twitter to facilitate the activity was to gather data and analytics by using the #mscedc hashtag and some Twitter-related data archiving tools.
I had mixed feelings about participating in such an expanded forum. A combination of fears such as exposing my learning to a huge and unfamiliar mass of people, time constraints and a 140 character messaging limit all contributed to my less-than-average participation throughout the duration of the activity. Overall however, I felt that I had made a decent contribution to the Tweetorial.
The Tweet Archivist data added a much needed context to a seemingly fathomless digital abyss. An immediate example of a surprising statistic was that around 700 (at time of writing) tweets were posted during a 19 day period. In my self-defined role as a ‘small contributor/big lurker’ at no point during the Tweetorial did I ever feel aware of the high volume of activity going on around me. It is only on reflection that I consider this statistic to be accurate. I find it interesting that the total number of text based contributions during the Tweetorial mirrors that of an average discussion forum that I observed within the ‘Internet of Things’ MOOC. Despite this similarity, I cannot say that I was aware of the same “digital cacophony” (Milligan 2017) that I experienced whilst conducting the micro-ethnography on the IoT MOOC.
The Tweetorial can be considered a success when comparing the final analysis with the objectives identified prior to the start of the activity. The aim of the Tweetorial was to conduct “some intensive tweeting around the ideas raised in weeks 8 and 9 of the course”. The top word analysis successfully identified and summarised the key words and discussion topics that have emerged throughout the preceding 8 weeks of the Digital Cultures course.
Some of the final statistics cast a sobering effect over me when I contrasted them with my own evaluation of contribution to the Tweetorial – most notably with the top user and user mention statistics. Prior to reading the final analysis I was content with my contribution and felt that I had contributed to most discussion threads and had a decent input to the Tweetorial. However after realising I was ranked 18th (out of 25) in the top user table and that I did not feature in the user mention rankings at all I felt somewhat deflated. Based on this, I felt relatively insignificant to both the activity and to the wider Twitter community whilst also feeling slightly embarrassed and disappointed in myself. As Kohn (1999) suggests, exposing students to ranking systems turns education into a competitive process rather than a learning one.
As I sought solace I investigated the analytics associated with my own Twitter account. I was uplifted after reading that during the same 19 day period my own tweets:
- had 3600 impressions
- received 39 likes (avg 2 per day)
- received 15 replies (avg 1 per day)
From an individual perspective I was generally happy with these statistics and was relieved when I compared them with the same metrics for the group. I was therefore afforded the opportunity to appreciate that general analysis of big data often neglects the circumstances and performance of the individual. Though my performance was considerably lower than that of my peers I certainly felt that I constructed knowledge and make a contribution to the Tweetorial with which I am happy.
In conclusion, as a learner I feel that there was little educational value in having access to analytic data of my performance within the Tweetorial. If anything, reviewing the data made me feel apprehensive and worried about my performance in comparison to my peers – whereas my individual analysis proved to be quite pleasing. I felt that I had contributed enough to both learn from and contribute to the activity, the only doubts that I had were as a direct result of comparing myself with others.
Due to the nature of the activity I felt very limited by having no opportunity to re-visit the Tweetorial and make additional contributions to alleviate my concerns. However I do wonder if further learning could be achieved if I had the opportunity to make more contributions. I could potentially fall into the trap of tweeting for the sake of tweeting, just to improve my statistics which would have little or no benefit for either the group of myself.
Kohn, A. (1999). From Degrading to De-Grading. Retrieved: 24 March 2017. http://www.alfiekohn.org/article/degrading-de-grading/
Milligan, S. (2017). The Internet of Things MOOC’ – First Impressions. Retrieved 24 March 2017. http://edc17.education.ed.ac.uk/smilligan/2017/02/12/the-internet-of-things-mooc-first-impressions/
This week’s Tweetorial highlighted areas of Learning Analytics (LA) that I was interested in investigating further – in particular ethics and student intervention.
Until recently I had a vague awareness of a JISC funded project aimed at developing a Learning Analytics service for UK Colleges and Universities (Jisc, 2015). I decided to delve into the project’s Code of Practice to gain a clearer understanding of how the education sector currently addresses some of the issues that we have been discussing this week.
During the Tweetorial, James Lamb asked the #mscedc group:
I responded by tweeting:
Therefore, I was relieved to read that JISC acknowledge that “Institutions recognise that analytics can never give a complete picture of an individual’s learning and may sometimes ignore personal circumstances”.
What I also found to be of high interest when reviewing the Code of Practice was guidelines relating to student access to analytical data. JISC stress “If an institution considers that the analytics may have a harmful impact on the student’s academic progress or wellbeing it may withhold the analytics from the student, subject to clearly defined and explained policies.”
I found this fascinating as we have been considering the potential consequences for students based on the comparison between analytical output and an institution’s performance benchmarks. What I hadn’t considered is how a student’s performance may be affected by viewing their own analytical data.
JISC. (2015). Code of practice for learning analytics. Retrieved: 18 March 2017. https://www.jisc.ac.uk/sites/default/files/jd0040_code_of_practice_for_learning_analytics_190515_v1.pdf
The following video raised some interesting points that I will investigate further when considering learning analytics and big data this week:
- How big is big data?
- What to be careful not to miss when using big data?
- Why do patterns emerge from big data and do we address them and learn from them?
Professor Maria Fasli reminds us that that we should ‘mind the gap’ between big data and hypotheses to avoid missing the opportunity to discover new knowledge.
I wanted to learn a little more about how sites such as Amazon compile their suggested product lists based on other people’s spending habits.
I came across this lecture delivered via a MOOC by the University of Washington. The lecture clearly explains that it (broken down into basic form) is simply a case of counting the number of shoppers that bought any combination of products and offering the most popular items as last minute add-ons.
I found this very helpful when considering the building blocks of algorithms. It would appear that in this instance “Big Data” is being used for creative analysis for the benefit of the masses (Enyon 2013). However there is certainly scope for acknowledging that there are other behaviours within an online shopping experience that may not be identified by spotting trends.
I hope to cover such behaviours and trends by conducting a small experiment and documenting my findings before the end of this week.
Enyon, R. (2013). The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology 38(3): pp. 237-240.
I chose ‘The Internet of Things’ (IoT) MOOC delivered by Kings College London via FutureLearn to be the subject of my micro-ethnography. I decided upon this MOOC as my IT background would act as a point of reference when wading through the mass content that is held within the course.
Before beginning my research, I ensured that I had taken appropriate steps to satisfy any ethical considerations arising from ethnographic observations. I contacted both the course facilitator, Prof. Mischa Dohler and the General Enquiries contact at FutureLearn to declare my intentions and make them aware that I was conducting an ethnography.
To begin, I felt it compulsory to establish just how massive the IoT MOOC was. I was forced into contacting Prof. Dohler as the information required wasn’t readily available to students enrolled on the course. I was intrigued by the idea that 8566 individuals were participating in an environment without having a full understanding of its scale.
Amongst the masses, there would inevitably be wide and varied sources of motivation for participation. The goal for what seemed like the overwhelming majority of participants was business opportunity or financial gain – however there were mentions of other motivators:
I decided to focus my ethnography on the role that discussion forums play in developing a community culture within a MOOC. In preparation for this I charted the relationship between the total number of comment contributions with the chronology of each forum. My findings were consistent with Fischer’s (2014) observation that the participation rate within a MOOC is usually always low.
From this I was able to make some important observations.
The cause of the constant decline in participation was difficult to prove without statistics being readily available. Instead I was able to make a comparison between the aforementioned motivators and Kozinets when he surmised that ‘if future interaction is anticipated, participants will act in a friendlier way, be more cooperative, self-disclose and generally engage in socially positive communication’ (Kozinets, 2010, p 24).
I noted that community building and academic discourse did not appear to be of any priority to those who admitted to enrolling on the MOOC to generate money. Instead, their forum comments contributed to what I previously referred to as “digital cacophony“. The result was a linear community with large volumes of people voicing their opinion without appearing to interact or engage with others.
Interestingly I noticed that each discussion forum was either not introduced, or introduced with a closed question, such as:
The resulting answers and opinions arrived in large volumes but there was very little interaction between any respondents. I put this down to the following reasons:
- participants seemed to want to satisfy their own needs rather than assist in the learning of others
- participants were not encouraged to challenge opinions and ask questions of each other
- there were simply too many comments to interact with and people seemed overwhelmed
In conclusion, although I was not an active participant in the MOOC I felt largely insignificant as a learner and almost unable to make sense of what was going on. The discussion forums were the only way to communicate with others on the course but I felt that the design of the course, student motivations and lack of direction had a detrimental effect on the community culture within the course.
Baggaley, J. (2014). MOOCs: digesting the facts. Distance Education 35(2): pp. 159-163.
Fischer, G. (2014). Beyond hype and underestimation: identifying research challenges for the future of MOOCs. Distance Education 35 (2): pp. 149-158.
Kozinets, R. V. (2010). Understanding Culture Online. Netnography: doing ethnographic research online. In Netnography: doing ethnographic research online. (London, Sage): pp. 21-40.
It’s late and I’m tired. I’ve done enough reading for today so thought I would watch a TED Talk before going to bed. Now I find myself updating my blog.
In the video above Ellen Isaacs is explaining the need for ethnographic observation within both technology design and differing environments. I couldn’t help but pay particular attention to the following offerings during her talk and loosely relate them to the culture within my MOOC:
1 – Human behaviour
Do people engage in the way that they think they are?
If an ethnography is an observational study of people’s behaviour in a community or environment, then I have been wondering if informing course participants that they are the subjects of research would influence their behaviour, and thus, not giving a true reflection of their behaviour. In the case of the IoT MOOC I suspect that I have went unnoticed – however it is something I have considered nevertheless.
My earlier posts have suggested that I am struggling to understand how people can construct knowledge in a connectivist MOOC without participating in any discourse whatsoever. In the case of the MOOC, I can personally relate to Fournier et al (2014) when they noted that around 1/3 of MOOC participants either found listening and reflecting or lurking as effective learning strategies. I fully expect to learn a little about IoT as a result of observing the MOOC but not actively participating. Whether that learning is correct is another matter.
I couldn’t help but compare the street sign examples in the video to the course content of the MOOC. What if the community within the MOOC was being influenced by differing understandings and interpretations of the static text and video within the course? After all, there will inevitably be people with differing experience, existing knowledge and (as previously tweeted) levels of English fluency within the MOOC. In other terms, I think until now my mind has been too focused on how the community is forming under its own weight without considering other factors such as course design.
Fournier, H., Kop, R., and Durand, G. (2014). Challenges to research in MOOCs. Journal of Online Learning and Teaching 10(1): pp. 1-15.
I have not long finished the Kozinets chapter entitiled ‘Understanding Culture Online’, Netnography: doing ethnographic research online‘ and found it very easy to relate to some of the observations that he noted throughout his studies.
Over the past decade I have been a member of different online communities ranging from personal interests (such as football related forums) to work-related groups (such as user forums). Until completing the Kozinets chapter I hadn’t ever really stopped to think about the dynamic of each community and the types of relationships that members within them form.
“How deep, long-lasting, meaningful and intense are those relationships? Are these people considered to be merely somewhat-interesting strangers, or are they long term friends that are as close to the participant as anyone else in their life?” (Kozinets, 2010, p 32).
This quote has been ringing in my ears since I read it. For me, the answer to this question influences the formation of a community and the development of a natural synergy.
Anyway, whilst lying in bed the other evening I spent some time reading through my Twitter timeline and eventually went down the rabbit hole (randomly diving into random conversation threads without any clear idea of where I was going) and stumbled on a link to a conversation thread on a parenting community called Mumsnet. In this thread there were several parents debating their opinion of a particular topic started by a current member. As the debate went on people were challenging, agreeing, disagreeing, dismissing, and praising each other based on their contributions to the thread. There was a level of interaction that allowed other members to consider changing their own opinion or forming new ones based on the experience and opinions of others.
In contrasting the Mumsnet community with the ‘Internet of Things’ MOOC community the difference is immediately noticeable despite having striking similarities. Both communities make use of discussion forums and both forums take a Q and A approach. In Mumsnet, a member asks a question and peers reply. In the MOOC the tutor asks a question and the students reply. Yet there is a distinct lack of interaction in the MOOC.
Could this be because of the reason that people join these communities?
When considering virtual worlds, Kozinets (2010) suggests that they are “structured so that social intercourse is the primary pursuit and objective” and that communities will therefore naturally form through discussion and interaction. However, a MOOC’s primary pursuit and objective, it could be argued, is personal interest and gain where social interaction plays a lesser role. Maybe this is the reason I am noticing such differences despite their similarities.
This is something that I will definitely be considering when conducting my micro-ethnography.
Kozinets, R. V. (2010). Understanding Culture Online. In Netnography: doing ethnographic research online. (London, Sage): pp. 21-40.
I’ll leave you with these (rather humerous) observations of the Mumsnet community that I sourced from Twitter:
Ethical considerations – Requesting permission from facilitator and provider
Marshall, S. (2014). Exploring the ethical implications of MOOCs. Distance Education 35(2): pp. 250-262.
February 17, 2017 at 10:19PM
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