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Final Reflection. Week 12

Reflection: Looking through a lens (Image: @bodzofficial Instagram)

A hundred years after Dewey published his book Democracy and Education (1916) championing education as a communal process, I wonder how the process of being a scholar of education in the digital age compares now to how it did then. The key principle of reflection in Dewey’s theory is still relevant today. Dewey claims that ‘[e]ducation, in its broadest sense, is the … social continuity of life.’ (p 4), since we live so much of our lives online it makes sense that educational communities have evolved and that we study them there.

The pressure on academics to publish using different mediums shows that scholars are required to do much more than thinking and writing alone. They are tasked with ‘new ways of working and new ways of imagining [themselves]’ (Fitzpatrick, 2011, p 3). This was certainly true in the use of a lifestream blog as a scholarly record. The constant pressure to be creative by publishing in a range of mediums and working quickly to meet tight deadlines is what it means to be a scholar in a digital world.

In Cybercultures we discussed how discourse contributed to instrumentalism (Bayne 2014) in relation to digital education. The discourse around ‘enhancement’ evolved into how our bodies are being changed by technology this was echoed in my visit to a Learning Technologies Conference on Health Education. We looked at how we are no longer limited to text when trying to portray scholarly thought (Sterne 2006) and I was able to do this by creating digital artefacts. It was interesting to see how other participants were able to construct meaning in ways I did not anticipate.

Community Cultures allowed us to see how educational communities are constantly evolving. The Massive Open Online Courses in which we participated supported our roles as researchers and students. Here we could see how digital education is changing and how cMOOCs have morphed into more individualistic xMOOCs over the last few years and have evolved to be smaller, less focused on community and more geared towards promoting participating universities and encouraging employability.

In Algorithmic Culture we reviewed how algorithms relate to pedagogical issues like sequencing, pacing and goal setting and evaluation of learning (Fournier 2014) and how these algorithms help our machines ‘remember’ us thereby determining the content we access. The discourse around Learning Management Systems (LMS) and their effectiveness to capture data (Siemens 2014) about students and their learning was reminiscent of discourse mentioned in Cyberculture.  The way in which institutions track and monitor students by using data echoed the issues around discrimination and invisibility I looked at earlier in the course.

I was daunted and anxious about my lifestream at the beginning of the course; having to do so much, so publicly was overwhelming. Seeing what other people did also inspired me. Having a reflective piece of work to map my learning is helpful as I can see how my development in my lifestream progressed. I feel it highlights not only my reflection (Dewey 1916), but my creativity and my technical skill. It has given me a new way of imagining myself as a student (Fitzpatrick 2011).

References

Bayne, S. (2014). What’s the matter with ‘Technology Enhanced Learning’? Learning, Media and Technology 40(1): pp. 5-20.

Dewey, J. (1916). Democracy and Education: An Introduction to the Philosophy of Education. Retrieved: 4 April 2017. https://s3.amazonaws.com/arena-attachments/190319/2a5836b93124f200790476e08ecc4232.pdf

Fitzpatrick, K. (2011). The Digital Future of Authorship: Rethinking Originality. Culture Machine 12: pp. 1-26.

Fournier, H., Kop, R. and Durand, G. (2014). Challenges to Research in MOOCs. Journal of Online Learning and Teaching, 10(1), pp. 1–15.

Siemens, G. (2013). Learning Analytics: the emergence of a discipline. American Behavioral Scientist, 57(10): pp. 1380-1400.

Sterne, J. (2006). The historiography of cyberculture. In Critical cyberculture studies. (New York University Press.) pp. 17-28.

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Tweetorial: A critical analysis

We can’t use data alone to measure student success.

The data from Tweetorial was graphically presented in charts and lists. It was easy to understand but it is limited in what it records for educational purposes. The analytics tool can only measure participation of those students who are active (tweeting, retweeting and responding) in Twitter. It provides no information about those who are passive (scrolling, liking and direct messaging) in the environment. The analytics are problematic because they contribute to the visualisation of participation but not necessarily learning and rate students against one another.

Our Tweetorial analytics consisted of data comprising of top users, top words, top URLs, source of tweet, language, volume over time, user mentions, hashtags, images and influencer index. This kind of analytic data is helpful when showing ‘what people actually do’ (Eynon 2013), for example who tweeted the most, what words they used in their tweets, where they got the information they tweeted about from, what language they tweeted in, how many tweets they produced, if they were mentioned by others, what hashtags and images they used and how many followers they have. It is more problematic when looking at the content of the tweets and measuring learning. Perhaps, a tool like NVivo would be helpful in trying to pull together the quality of the content being discussed but this still limits the understanding because not all participants’ learning is evident as content can only be measured through active participation.

There is a flaw in the Tweetorial analytics; students who did not actively participate were not included in the data. If we compare the Tweetorial to a traditional tutorial, the tutor could ask the same questions, in both environments there will be students who dominate the conversation and those who are more comfortable to watch and not actively contribute. Those who do not actively contribute are still present. This is not measured in the Tweetorial analytics.

It was interesting to see that one of the students in our cohort, who could be perceived to have been inactive in the Tweetorial was also very quiet in the Hangout tutorial. As an ethical consideration, I will not name the individual. In other tutorials in which I have participated, this individual has contributed much more and I have to wonder whether they were more withdrawn because the analysis did not show them in a favourable light and they felt reluctant to contribute. I have subsequently looked at their own blog about the Tweetorial and their weekly synthesis, both make for very engaging reading and brought a unique perspective to my own scholarly thought. They mention their inactivity but this did not seem to affect their learning. This person is clearly engaged with the course and has made excellent contribution but not in the space that was being measured. The data does not therefore represent reality accurately.

Part of the problem when one is a student using an open educational space for learning, is the acceptance of vulnerable position of having your academic work being available for both your peers and the online world; the online world is far less of risk because the likelihood of them being interested in what you are talking about is substantially less than having your work being visible to your peers. Peer review is a common academic practise but for those working outside academia and not necessarily wanting to pursue a career in academia, this openness can be daunting. In an open course such as Education and Digital Cultures, students can often feel the added pressure of their peers judging not only the quality of their work but also their participation. While this outlook is probably exaggerated for me personally, the public nature of the participation of the Tweetorial overall motivated me to take part. I felt relief that my participation had been recorded but at the same time I struggled with the competitive nature of learning in an open environment.

The visualisations, summaries and snapshots are measuring participation and although they are not ultimately measuring performance these visualisations are similar to grades, rating student success. There are particular issues with using analytic data in this way, not least of all that if students get graded poorly in front of their peers, this can lead to resentment, anger and demotivation (Kohn 1999). The most interesting factor is that the results of the Tweetorial do not actually measure learning so neither my peers nor my tutors can see how much I had attained, nor could we see that attainment for others.

As educational researchers, the content that is provided by analytic tools such as the one used in the Tweetorial limits the kinds of questions we can ask about learning (Eynon 2013) because the recording of learning in these environments is problematic. We can only study what is recorded and we can only ask questions around that data. The data presents a snapshot and it is related to participation and not attainment. If our research focuses on how students learn, we have to build relationships with those students as in order for data to be effective because it needs to be interpreted in context through observation and manipulation (Siemens 2013).

The data that is presented will allow teachers to be able to identify trends and patterns exhibited by users (Siemens 2013), this will then allow tutors the opportunity of adjusting the course accordingly. Although this was not exhibited as such in the Tweetorial, our discussion around cheese could similarly be related. If the tutor was able to see that content which was not explicitly related to the course being discussed, they could adjust their questions or add additional content accordingly.

Analytics tools only provide information for part of the students’ experience. Although useful, this data should be used in the context of the greater course. It needs to be interpreted concurrently with other data gathered through observation and evidence. It can assist the tutor with being able to monitor the trajectory of the course and show who is actively participating but it is limited when trying to establish attainment. Tutors should also be mindful that data such as that presented in our Tweetorial can also affect student motivation and participation.

Eynon, 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.

Kohn, A. (1999) From Degrading to De-Grading, Retrieved 24 November 2016. http://www.alfiekohn.org/article/degrading-de-grading/

Siemens, G. (2013) Learning Analytics: the emergence of a discipline. American Behavioral Scientist, 57(10): pp. 1380-1400

 

 

 

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Co-constructed ecosystems Week 8

ecosystem
Photo: Flickr @giveaphuk

I started my week trying to find out what exactly algorithms were. I had a vague understanding that they were part of the coding that looks for patterns and then changes functionality of certain online spaces, usually to do with shopping and social media. I’ve mostly come across them through social media feeds where influencers are usually advocating for you to turn notifications on about their posts. What surprised me when I started looking for information about how algorithms work, almost as often information on how to manipulate them popped up.

I was trying think about how algorithms may influence education and where they might fall short when I stumbled upon the amazing Joy Buolamwini. She highlighted the real consequences of how having a lack of diversity in programming can impact technology in ways we do not expect. It was evident from her experience that technology rendered her invisible by not being able to read her features. I wonder how many other invisibilities are not yet evident.

We met for our weekly Skype group and some of the bigger themes emerging from that conversation were about how algorithms are used for control and surveillance. We wondered if this might cause students from certain, ethnic, socio-economic backgrounds to be marginalised.

The TED talk on How algorithms shape our world. Was really insightful on how algorithms link. The ‘ecosystem’ metaphor Slavin used echoed Active algorithms: Sociomaterial spaces in the E-learning and digital cultures MOOC (Knox 2014).

It was in this vein I found Hack Education’s article about the Algorithmic Future of Education. Watter’s highlights how the marketization of education and how important ‘care’ is when dealing with students.

I rounded the week off working with Stuart by comparing how algorithms work in different online spaces.

Knox, J. 2015. Algorithmic Cultures. Excerpt from Critical Education and Digital Cultures. In Encyclopedia of Educational Philosophy and Theory. M. A. Peters (ed.). DOI 10.1007/978-981-287-532-7_124-1

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Comment on Stuart Milligan’s Micro-ethnography by cpsaros

Hi Stuart,

Great post, l like how you managed to incorporate lots of different kind of media for an engaging post.

It was really useful to do this course with you. Kudos for sticking with it! I don’t think I would have stuck with it as long as I did without your insightful observations. You summed up what it was like being on the course very accurately.

It was interesting to experience the different dynamics of the two courses (EDC and IoT) with the same person. I thought it was fascinating that we were never able to connect on the IoT. Had we not had the connection we did from EDC, we would not have been aware that the other was on the course. Although I did feel that we were guilty of a bit of ‘jiggery-pokery’ and colluding behind the scenes ;).

from Comments for Stuart’s EDC blog http://ift.tt/2mvzdir
via IFTTT

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Communities. Week 7

The Indignados used social media to mobilise. Photo: @thecommenator

I attended a Digital Cultures seminar, The People’s Memes: Populist Politics in a Digital Society held at King’s College London. There were interesting comments about how political movements developed out of what were the inequalities and disenfranchisement felt by those outside of the political elite. Digital communities like the Indignados who were the birthplace of Podemos, a Spanish party to form a more accessible alternative. What I found particularly interesting about the research being done in this field, is that much of the hierarchical systems that these new movements were responding to with regards to inequalities and inaccessibility, is now being replicated online. I thought this example linked well to the Knox (2015) paper and how technology is seen to become ‘anti-institutional and emancipatory’ but in fact just continues to replicate what is already present in society.

After receiving feedback, I commented on other participants’ blogs, trying to get inspiration so I could link more feeds with IFTTT to my lifestream.

On Wednesday, a few of the participants had a Skype chat to share what feedback they had received about their lifestream. It was here, talking to others, that I realised that a narrative for my lifestream synthesis was more about what I had posted and less about what I was thinking.

This interaction with my peers and my dabbling within my MOOCs lead me to question how communities are built? Which is why I bookmarked the Abbott (1995) paper Community participation and its relationship to community development on Diigo.

Most experiences of MOOCs seemed to be negative which lead me to question if they are sustainable.

Finally, I browsed the ethnography posts within MSCEDC so get inspiration for exhibiting my own.

References

Abbott, J (1995). Community participation and its relationship to community development. Community Development Journal 30(2): pp158-168.

Knox, J. (2015). Community Cultures. Excerpt from Critical Education and Digital Cultures. In Encyclopedia of Educational Philosophy and Theory. M. A. Peters (ed.). DOI 10.1007/978-981-287-532-7_124-1

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Diigo: Community participation and its relationship to Community Development

ABSTRACT The objective of this paper is to define the relationship between community participation and community development. The paper illustrates the weakness of existing interpretations, arguing that they are flawed because they concentrate on the failings of community development without analysing why successful community development succeeds. The paper concludes that community development is actually a specific form of community participation, the success of which is determined by two key factors: firstly, the role of the state; and secondly, the complexity of the decision-making taking place at the core of the community participation process.

http://ift.tt/2lhhYS9

As I reflected this week, I wanted to find out more about what we mean by ‘community’. Is community a group of people grouped together by a commonality, like race, religion or ethnicity? Or ido individuals involved have to have some kind of shared value system or interest? What makes a community? Are those engaged within it responsible for community development or should it grow organically? This paper did quite answer all those questions but it added to my thinking on the subject.