Critical Data and Education

Course Details

Course code: REDU11091

Course leader: Dr Jeremy Knox

Course delivery: Jan 2021, Sept 2022


Recent years have seen a growing interest in using data collected from a range of information technologies to intervene in educational activity, with the intention of producing organisational efficiencies, making more precise pedagogical interventions, and enhancing student experiences. This course will surface important critical perspectives needed to examine how such technologies influence decision-making, from educational policies to everyday classroom activities.

This course will draw on literature from the emerging area of ‘critical data studies’ to engage students in an in-depth examination of the potential impact of data-driven technologies on educational policy, teaching practice, and student experience. Students will learn concepts and theories used to make sense of data-driven technologies and their influence on organisations and individuals, as well as practical skills in creating and analysing data directly related to their own activity and experiences on the course. This will enable students to critically understand and evaluate a range of data-driven technologies and associated practices related to their professional contexts.

Learning Outcomes

On completion of this course, students will be able to: 

1. Demonstrate a critical understanding of how data is defined, produced, analysed, and understood in educational contexts.

2. Exhibit a critical awareness of key data-driven technologies and practices as they relate to educational governance, institutional administration, and the activities of teaching and learning.

3. Identify and critically analyse published research 

4. Engage critically and creatively with practical approaches to data collection and analysis

5. Effectively discuss, analyse, and evaluate key issues related to the use of data in education, demonstrating the conventions of academic discourse


The Critical Data and Education course is currently in development - a detailed structure will follow. The course will be 12 weeks in length, and organised around three thematic blocks.


100% coursework

Part 1 (50%): The design, implementation, analysis, and evaluation of an individual data collection strategy, directly related to course participation. Students will begin collecting data, in digital or analogue form, after the introductory teaching block, and maintain a record of this activity throughout the subsequent taught weeks of the course. This archive of data will be submitted for assessment, along with a 1000-word accompanying essay reflecting on the process, analysing the results, and evaluating the implications for educational practice. 

Part 2 (50%): A ‘digital artefact’ final assignment, critically reflecting on a chosen theme from the course. The submitted work will be multimodal and presented in a digital online format, in a form equivalent to a 2000-word essay. The assignment will demonstrate critical engagement with key concepts from the course, use of relevant and appropriate literature, and exhibit the construction of fitting academic discourse.

Teaching Methods

The course will be delivered using WordPress, and students will be allocated an individual blogging space. Teaching methods include a combination of synchronous and asynchronous discussion, individual and group contact, twitter tutorials, and peer feedback.


Eubanks, V. 2018. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. Macmillan.

Eynon, R. (2013) The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology. 237-240

Kitchin, R. 2014. The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. London: Sage.

Knox, J, Williamson, B & Bayne, S 2019, 'Machine behaviourism: Future visions of learnification and datafication across humans and digital technologies', Learning, Media and Technology, pp. 1-15.

Lupi, G. & Posavec, S. (2016). Dear Data. Penguin.

Mackenzie, A. 2017. Machine Learners: Archaeology of a Data Practice. London: MIT Press.

Noble, S.U. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press.

O'Neil, C. 2017. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. London: Penguin.

Williamson, B. 2017. Big Data in Education: the digital future of learning, policy and practice. Sage.


As with all courses, you will be required to have regular access to a computer with a good broadband connection, and will be responsible for providing your own computing equipment and consumables. All core and some additional readings will be provided online.