2) Targeted advertising based on your demographic and what you ‘liked’ in the past and
3) Deep learning-to manage community moderation.
Deep learning looks at words in the comments in posts, it groups the words together and considers what is good and bad text. Bad text might be what it considers trolling, hate speech or words associated with cyberbullying. The algorithm is a closely guarded secret, we do not know how the model works or what type of comments they are targeting or how many comments are being removed. Therefore we don’t know how biased it is, and how much this algorithm is “used to coerce, discipline, regulate and control: to guide and reshape how people, … interact with and pass through .. systems”. Kitchin 2017
Kitchin, R. (2017) Thinking critically about and researching algorithms, Information, Communication & Society, 20:1, 14-29, DOI: 10.1080/1369118X.2016.1154087
Thanks to Irene (previous class?) for sharing this
In this video Mr. Agarwal discusses 5 key points to helping students successfully pass a MOOC.
Active learning- 5 minute videos followed by interactive exercises- when students answer questions, they are learning.
Instant feedback- the green tick is positive reinforcement & turns teaching moments into learning outcomes.
Self paced learning- students can rewind, so they don’t get lost.
Gamification through drag and drops can simulate a lab environment and changing scenarios.
Peer learning via discussion boards. When students respond to each other’s questions, they are learning by teaching other students.
The MOOC that I am studying has put structures in for the first 3 elements. Gamification is not included and item 5 discussion boards are visibly present but under utilised because online community relations are underdeveloped (see Kozinets review),Weaker social ties and weaker consumption is linked to the weaker skill set of a lurker (Kozinets 2010) so this is an area that Moocs offer promise in but have yet to take full advantage of. In a blended learning environment, the gamification element would take place in a real lab environment via a -flipped classroom setting.
In Mr Agarwal’s study, failure rates fell from 40/41% to 9% using the flipped classroom model. So blended learning using a flipped classroom technique that facilitates students working together in labs, still seems to be the most optimal type of learning.
Whilst Mr Agarwal believes in the power of peer learning in Moocs via discussion boards, this element of the MOOC still needs teacher presence in order to offer guidance and prevent misinformation from peers, an impossible task for courses of thousands of participants.
Another thing I note is Mr Agarwal’s plan to license his successful MOOC to other universities. Whilst it would be good to cherry pick lessons to supplement learning, the result of several major institutions adopting the same MOOC, to teach the same subject would be a homogenising of the learning from one source. This reminds me of an article written by jiyoung Kwan on gender and language imbalance on WIKIs. What is right or wrong or missing on Wikipedia affects the entire internet.
some other elements not mentioned but useful for interaction are; Twitter hash tags, a wiki for sharing articles and a poll to gauge what learners already know or how they feel about the topic.
Bayn, Nancy K. (1999) Tune In, Log On: Soaps, Fandom, and Online Community. Thousand Oaks, CA: Sage. As quoted by Kozinets (2010)
Walther, Joseph B. (1992) ‘Interpersonal Effects in Mediated Interaction: Relational Perspective’, Communication Research, 19: 52–90. As quoted by Kozinets (2010)
Lofi is well loved by second level students and those who are studying at their desks.
The stream is live and at the time of writing, 31, 755 are listening with me, many of them studying too. I get a sense of young people dreaming, imagining, building around me as I listen with them.
Thanks to my classmates Susanne and Val for your posts on Cyborg Feminism. It’s not just about the different ways that male and female cyborgs are popularly portrayed, it is the general questioning of power structures outside of male and female. Val linked to an article (Feminism and Cyberculture) which said “information technology often presents itself to us as potentially liberating when in fact our actual interactions with it often reinforce conventional social structures of domination”.
Dr Glabau’s you tube talk linked from Susanne’s post then spoke about the actor network theory where it’s not just humans in a technological system but also non human laborers, who contribute example animals in the food chain. Some animals are doing the work, some enjoying the technology, some exploited in that network. One of the questions cyborg feminism asks is who is creating these technologies, to what end, who is receiving opportunity or repression? Dr Glabau asks us to consider where are the voices of all beings represented in the network so that every voice is heard in designing technologies for the future. The motivation for implementing technology shouldn’t be just instrumental and economic. It’s not that straightforward.
The title of the video is humans need not apply; this video is a review on how Mechanical minds will push humans out of the economy. Currently we have no plans in place for when when large sections of the population cannot get employment. How dystopian do we want it to get?
Instead of accepting a future where the digital devices ‘define and govern how people use them, termed ‘technological determinism’ (Dahlberg 2004 as quoted by Knox2015)’, we could find a way of coexisting so that every human can maintain a sense of purpose and leave a legacy built on life’s work and passion; it is part of what it means to be human.
The mechanical minds that leave human brain labour less in demand. Ranging from to transportation bots (self driving cars) to doctor bots to creativity bots. #mscedlhttps://t.co/Lbr4PTvPP6
Dahlberg, L (2004). Internet Research Tracings: Towards Non-Reductionist Methodology. Journal of Computer Mediated Communication, 9/3., as quoted by Knox, J (2015) chapter1. http://onlinelibrary.wiley.com/doi/10.1111/j.1083-6101.2004.tb00289.x/full
