I decided the best way to summarize how algorithms have influenced my livestream would be to have a closer look into my fellow students’ conclusions on algorithms and comparing them with my own experiences.
They played with You Tube, Netflix, Google, etc., algorithms and meddled with their recommendation feeds (video, audio, friends, ads, courses). The most interesting fact was that MSCEDC students had very similar experiences and many recurring conclusions on algorithms I similarly noticed.
Technology is not a passive “tool” as it used to be considered at the origins of Community Cultures (see Knox 2015, page 1). Algorithms are still making mistakes but autonomous reactions to personal behavior are identifiable.
Algorithms are shaping our reality. The “AI” is not the unknown force as it was considered during Cybercultures. Humans openly accept machines to take decisions for them.
Algorithms are in many cases not as objective as originally intended and show clear evidence of bias by economical or other interests. Due to their multilayered and highly complex design the authority behind the algorithm is non-transparent (see Williamson 2017 and Knox 2015, page 1
Although many algorithmic propositions seem misleading, wrong or deficient, there is a suspicion that they are still intended by hidden interests.
The entanglement of the agencies is leading to a reduction of cultural diversity and the production of knowledge (Knox 2015).
The assumption of the autonomous learner as guiding principle for the design of algorithms.
Algorithms provide a predetermined path, supporting a “goggle vision” leading to reduction of variability of search results and looping.
When developing and implementing my livestream, I witnessed most of these conclusions myself. I used You tube, twitter and google search frequently for research and their algorithms influenced my selection, my reality and therefore the outcome of my liveblog. “You Loops” or “Echoing” was frequently observable, inefficient results and predomination of certain information sources detectable. Troubleshooting of malfunctioning ITTT algorithms was difficult to solve due to multilayered connections. This reality shaping power algorithms have were well described by Kitchen (2007, page 15).
Although we tend to struggle with the accuracy or the missing information about hidden agendas, the use of algorithm has – and will have – a potential to influence learning and teaching.
knows the learner and provides helpful alternatives (“You have skipped or stayed longer than average on this page, maybe you want to have a look into …”)
shows additional information of whatever format to support learning (“ you seem to like this, maybe have a look into…”)
changes the interface, course thumbnails, etc. due to user preferences
using educative nudges to make learners make favorable decisions (see Knox et al. 2020, p. 39)
works in symbiosis with the teacher as provider of unfiltered or partially filtered information.
In order to work with algorithm-based technology educationalists must maintain a critical perspective. The algorithm is currently shaping the culture to a great extend and education is not excluded. Therefore, teachers should accept the new status quo and openly engage with the technology.
Developing a liveblog is a good start!
Kitchin, R. (2017): Thinking critically about and researching algorithms, Information, Communication & Society, 20:1, 14-29
Knox, J. (2015): Algorithmic Cultures. Excerpt from Critical Education and Digital Cultures. in Encyclopedia of Educational Philosophy and Theory. M.A. Peters (ed.)
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
Knox, J., Williamson, B. & Bayne, S. (2020): Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning,Media and Technology, 45:1, 31-45, DOI: 10.1080/17439884.2019.1623251
Schmidt, E. and Rosenberg, J. (2018): How Google Works – Eric Schmidt and Jonathan Rosenberg, Retrieved from: https://www.alexjhughes.com/books/2018/3/11/how-google-works-eric-schmidt-and-jonathan-rosenberg, 29.03.2020
Williamson, B. (2017): Introduction: Learning machines, digital data and the future of education (chapter 1). In Big Data and Education: the digital future of learning, policy, and practice.
Algorithmic play of all MSCEDC 2020 students. Thank you very much!