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DRPS : Course Catalogue : Moray House School of Education and Sport : Research (EDU)

Postgraduate Course: Critical Data and Education (REDU11091)

Course Outline
SchoolMoray House School of Education and Sport CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course offers a range of critical perspectives on the increasing use of data and data-driven technologies in educational governance and practice. Recent years have seen a growing interest in using data collected from a range of information technologies to intervene in educational activity, with the intension 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.
Course description 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. Assessments will allow students to complete the course with 20 Credits at SCQF Level 11.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed:
Prohibited Combinations Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  None
Course Start Semester 2
Course Start Date 15/01/2024
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 196 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 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.
Feedback Feedback is staged throughout the course in the form of individual and group tutorials that support a number of non-assessed activities. This allows feedback to develop student understanding across the various sections of the course, building towards the two assessed activities. Peer feedback sessions in each of the thematic blocks also allow students to give and receive feedback in structure discussion.
No Exam Information
Learning Outcomes
On completion of this course, the student 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
Reading List
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.

Williamson, B 2019, 'Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education', British Journal of Educational Technology, pp. 1-16.
Additional Information
Graduate Attributes and Skills A. Research and Enquiry. To be able to identify, define and analyse conceptual and/ or practical problems in Digital Education through the critical appraisal of existing evidence. To be able to generate creative and innovative approaches to research appropriate to the broader context of Digital Education.
B. Personal and Intellectual Autonomy. To be able to exercise substantial autonomy and initiative in the identification and execution of their intended learning activities. To be independent learners able to develop and maintain a critical approach to issues in Digital Education.
C. Communication. To be make effective use of the multimodal capabilities of digital technologies to communicate appropriate knowledge and understanding of emerging concepts and practices in Digital Education.
D. Personal Effectiveness.To be able to recognise and respond to new opportunities for learning and development. To be able to work effectively with others in diverse digital environments for learning.
Keywordsdata,big data,analytics,AI,machine learning
Course organiserDr Jeremy Knox
Tel: (0131 6)51 6347
Course secretaryMs Annemarijn Huizinga
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