Tag: Tweetorial

Introduction

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.

Summary

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.

Analysis

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 18^th (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.

Conclusion

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.

References

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/