Transforming education
Some additional perspectives on the data-driven transformation of education systems across primary, secondary and tertiary levels.
In this TED talk viewed over 1 million times the game designer Jane McGonigal explains the potential impact which gaming principles can have on the world. Her book Reality is Broken introduces these ideas in greater depth. It can seem counter-intuitive to claim that gaming can have this significance but it's an increasingly popular idea. There is a wider sense of 'game' (""an activity that one engages in for amusement or fun" according to the dictionary") but video games are the most influential factor in the popularity of gamification. There are over 3 billion players of video games in the world who play a global average of 8.45 hours per week. What makes video games so engrossing? Can this be leveraged within education?
Gaming can make the world a better place
Games like World of Warcraft give players the means to save worlds, and incentive to learn the habits of heroes. What if we could harness this gamer power to solve real-world problems? Jane McGonigal says we can, and explains how.
This is a rigid view of education built around measurable standards and numerical feedback but with cute monsters.
Using gamification principles as a designer requires providing the right mix of engaging elements For example ClassDojo provides stickers for feedback and offers class rankings using cute monster avatars. It's a surveillance apparatus which enables the real time tracking of behaviour at individual and class level; including behavioural reinforcement through 'dojo points'. Teachers need to monitor students with the teacher/student interaction filtered through the platform. Have you used educational platforms which have game elements as part of their design? E.g. badges, leader boards, timed competition, turn taking, dice throwing, collecting currency to upgrade avatars Gamification can make education more engrossing but it also changes the nature of educational interaction by mediating teacher/student and student/student interaction through a platform
- Gamification in LearningThis piece by Bryan Jones offers a practical approach to implementing gamification for instructional designers. It illustrates what this looks like in practice at the level of pedagogy rather than simply being a matter of digital design.
- Critiquing GamificationThis paper by Sebastian Deterding addresses some of the problems involved in gamification as it tends to be conceived. It rejects an "additive, atomistic and deterministic conception of experience design" in favour of one built around human flourishing.
- Case Study of GamificationThis paper by Ben Williamson analyses ClassDojo as a case study of educational techniques including gamification. This is a fascinating example which it is worth exploring further if you plan to use the concept of gamification in your assignments.
Education, Learning and Data Science
The ongoing ‘learnification’ of education, is described by Biesta (2019) as the redefinition of all things educational in terms of learning. For example, calling students learners, calling schools learning environments or places for learning, referring to adult education as lifelong learning and seeing teachers as facilitators of learning. Biesta (2019) suggests that it is not just a change in the language (from education to learning) that has changed, but there is also a change in the role and position of the teacher from the ‘sage on the stage’ to that of a ‘guide on the side’. With this change in role students are considered to be a subject in their own right and not an object of the teacher’s actions.
“Learning, [in this sense], provides opportunities for students to be free and enact their freedom outside of the control of the teacher” (Biesta, 2019:550).
It is this learning freedom and the process of ‘learnification’ that Knox, Williamson and Bayne link together with the methods of data science, focusing on datification and machine learning.
The following paper in many ways encapsulates the key issues of the entire unit. It isn't an easy paper to read, so please do work with others to read it together and make sense of it.
Read: Jeremy Knox, Ben Williamson & Sian Bayne (2019) Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, DOI: 10.1080/17439884.2019.1623251
What is an algorithm?
An algorithm is a list of steps which tells a computer what to do. This makes it sound extremely simple but the functions of algorithms and their social consequences can be extremely complex in practice. There is an increasing awareness of the role of algorithms in society but this often rests on a sense there are shadowy entities manipulating us in the background, as opposed to being specific instructions coded into software and services. It is easy to be paranoid about algorithms but that doesn't mean they are not dangerous.
BOYZ
Introducing Algorithms
Algorithms are produced by people with assumptions shaped by their position within the world. This means that beliefs they hold (e.g. with regards to what 'good' teaching is) can have a significant impact on the world through the operation of the systems they have designed. They are increased used to make decisions who can take out finance, who gets interviewed for a job, how much insurance costs etc. But exactly how these decisions are made is rarely transparent and it is difficult to review.
They can be extremely crude in their operations e.g. Washington school district firing all teachers in 2009-10 whose outcomes put them in the bottom 2% This can reinforce inequality because there are factors other than the teacher's performance which are likely to shape outcomes e.g. classes in affluent areas likely to perform better than classes in deprived areas.
The video below from Cathy O'Neill explores how dangerous these issues can be in practice. Her Weapons of Math Destruction is an excellent accompaniment to the core text of Doing Data Science.
They can be extremely crude in their operations e.g. Washington school district firing all teachers in 2009-10 whose outcomes put them in the bottom 2% This can reinforce inequality because there are factors other than the teacher's performance which are likely to shape outcomes e.g. classes in affluent areas likely to perform better than classes in deprived areas.
The video below from Cathy O'Neill explores how dangerous these issues can be in practice. Her Weapons of Math Destruction is an excellent accompaniment to the core text of Doing Data Science.
