Week 9

The final set of cases and concepts

No required reading this week as you should now be working on your presentation. Please seek the week 6 list if you would like additional reading suggestions and explore reading below for topics you are particularly interested in.

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 Learning

This 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 Gamification

This 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 Gamification

This 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.

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.

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.

Massively Open Online Courses (MOOCs)

Massive open online courses (MOOCs) emerged in 2012 as a purported revolution in online education. Platforms like Coursera, Udacity, FutureLearn and EdX promised to transform learning through video lectures and scale. The New York Times dubbed 2012 the “Year of the MOOC,” with hyperbolic forecasts of these platforms wiping out traditional universities.

Martin Weller describes the “infinite lecture hall” vision underpinning MOOCs. The economics were appealing - delivering a lecture to 10,000 students cost nearly the same as 10 students. This content dissemination model reduces education to broadcasting information for students to individually absorb. MOOCs claimed revolutionary scale in broadcasting and data-driven tracking of learning.
However, the initial euphoria outstripped reality. MOOCs saw extremely low course completion rates, although unreasonable to directly compare to campus courses. The broadcast model suited an imaginary “roaming autodidact” learner, as Tressie McMillan Cottom critiques, but struggled to foster rich interaction. High quality seminars and tutorials were economically unviable. MOOCs relied on universities to generate knowledge, while replacing everyday instructors with “superstar” professors from elite institutions.

The initial MOOC mania exemplified misguided tech solutionism promoted by commercial interests rather than meaningful educational transformation. As the promises failed to materialize, MOOC platforms pivoted to credentialing and courting corporate learners. The utopian vision gave way to more modest uptake and conventional business models - a microcosm of the distance between EdTech hype and actual impact.

Silicon Startup Schools

Williamson's paper provides an incisive analysis of the growing encroachment of Silicon Valley priorities, ideologies, and practices into reshaping public education in the United States. He focuses on four prominent new charter schools that exemplify the tech sector's aspirations to fundamentally disrupt, reimagine and rebuild schooling in its own Computational image – moving from marginal improvements through technology implementation to radical reinvention of the school model itself. As outlined on pages 219-220, these startup schools operate as “scalable technical platforms, underpinned by software engineering expertise.” They are funded by a novel amalgam of venture capital and philanthropic donations from billionaire tech entrepreneurs. Their design and vision is guided by the technological assumptions and solutionist imaginary dominating digital elites’ aspirations to hack broken systems and code social problems out of existence.

These schools, detailed across pages 221-232, are characterized by: novel funding mechanisms enabling private takeovers of public infrastructure; data-driven software infrastructures rendering students and teachers increasingly visible to monitoring, analysis and ‘optimization’; contradictory messaging appealing to young people’s self-actualization alongside intensified surveillance; and insistent framing as testbeds solving education through entrepreneurial innovation rather than messy political struggle over resources and purposes. Through code and computational practices infusing all operations, they embody an emerging computational ontology recasting education as a technical optimization challenge.

By foregrounding these schools as harbingers of Silicon Valley’s growing definitional power over public education’s future, Williamson highlights the urgent need for updated theorization and critique attentive to how software, ideology and elite power are increasingly animating the purpose, governance and daily experience of schooling.

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