My lifestream blog contains a blend of sourced and composed resources that reflect the key themes of Education and Digital Cultures. To fully explore each theme I conducted a series of practical exercises to gain insight from both an institutional and individual perspective. The content of my blog highlights many different points of view on each theme and is reinforced by experimentation that ultimately allowed me to construct knowledge of each topic through experience.
Throughout the course I have questioned if, as human beings, we are supposed to benefit as individuals from digitisation – particularly when studying algorithmic cultures. In studying my own performance and analytical data from an online learning activity, I gained experience of the impact that exposure to learning statistics has on students. I realised that whilst big data and analytics support the notion that digital is better, within education this may only ring true for the institution and not the individual. This was an invaluable experience in connecting my understanding of the course themes to the content of my lifestream blog.
On conclusion, one could also observe a shift in digital culture over time. In the early stages the purpose of digitisation was to assist humans to do basic tasks. This gradually evolved into doing machines performing complex tasks and exceeding the limitations of human form. In the present, we are using technology as an alternative form of intelligence and as a tool for efficiency and predicting the future. Certainly, if transhumanism and cyberpunk ideologies come to pass, then the human form will play a lesser role in both education and the wider society.
Lister, M., Dovey, J., Giddings, S., Kelly, K. (2009). Networks, users and economics. In New media: a critical introduction.M. Lister (Eds.) (London, Routledge): pp. 163-236.
It was good to catch up with the group during this week’s Google Hangout. I always really enjoy discussing recent tasks and themes with my peers as I always find a new and interesting points to consider as a result.
One example of such points would be considering the difference between text based communications on Twitter in comparison to those within a MOOC. My hand-drawn diagram within the ‘#mscedc Digital Cacophony – Tweetorial vs MOOC’ post suggests that despite not being purposefully built for education, I found Twitter to be a more suitable forum for group discussion.
Following the Tweetorial I further investigated the need for analytics and big data within education. In the post entitled ‘Big Data, the Science of Learning, Analytics, and Transformation of Education’, Candace Thille noted that online environments encourage students to collaboratively move towards set goals whilst being able to synthesise knowledge to apply in new contexts. It is that ability that held my interest throughout the week and became a consideration that I took into my critical analysis of the Tweetorial.
For my critical analysis I examined the analytical data from the Tweetorial. I found myself comparing my performance to that of my fellow students and documenting my thoughts from both an individual and a collaborative perspective. The data would indicate that I made a lower than average contribution which on initial observation could be interpreted negatively. However I felt that I both contributed and received useful information throughout the activity and constructed new knowledge as a result.
I felt that this week afforded me the opportunity to gain first-hand experience of the topics and themes that I have been studying.
Big Data, the Science of Learning, Analytics, and Transformation of Education
From the mediaX Conference “Platforms for Collaboration and Productivity”, Candace Thille, with the Stanford Graduate School of Education highlights the power of platform tools and technologies to transform observation and data collection. This process enables researchers from industry and academia to know their user better – as consumers, as producers, and as learners.
via YouTube https://youtu.be/cYqs0Ei2tFo
This week’s Lifestream activity has been dominated by the group ‘Tweetorial’ in which we investigated some topics and issues highlighted in the recommended viewings and readings. In summarising my Tweetorial activity, I would note that I contributed to discussion threads surrounding the following key themes concerning Big Data and Learning Analytics (LA):
Social media influence on algorithmic culture
Big data influence over students
Algorithmic pattern identification
Dependence on analytics
I felt it essential to explore the vastness of Big Data and to consider the implications of identifying patterns when it is analysed. I felt that this week’s recommended material focused on either how data was gathered/analysed or the resulting consequences for students. Therefore, I became increasingly interested in the gap between big data and hypotheses and what new knowledge we can discover from the space in between. My ‘Analyzing and modeling complex and big data’ post attempted to address this issue.
Following on from the ‘Tweetorial’ I was motivated to explore some of the issues raised to put them into a relevant context. My ‘Learning Analytics – A code of practice’ post summarised my investigation into a JISC funded LA project in which the project team addressed many (if not all) of my concerns around ethics and student intervention. In hindsight, I had only really considered LA from the perspective of the institution and the learner – not of the individual as a person.
It was another enjoyable week and I’d like to thank my tutors and peers for a very engaging Tweetorial.
I’ve been studying all things algorithms this week and found it to be a massively complex yet fascinating topic. It almost feels as if it would be impossible to fully comprehend the scale and spread of algorithms and the influence they have on our daily lives.
To that end, this week’s content on my Lifestream blog has helped me to start make sense of it.
My ‘How algorithms rule the world‘ post helped me gain some perspective about how computer based algorithms can affect the physical lives of every day users. I firstly considered this from an educational point of view however my thinking expanded somewhat after considering the policing example within the article. I now feel that algorithmic culture has a direct influence on societal culture.
My studying for the week concluded with a mini-experiment that I conducted in partnership with Chenée. This was a great opportunity to learn first-hand about the amalgamation of social and material factors in influencing an online experience. Our findings complimented Enyon (2013) in that our options are often influenced by the trends set by the wider, global population.
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.