MA Educational Leadership / Course details

Year of entry: 2022

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Course unit details:
Big Data and Machine Learning Design in Education

Unit code EDUC71221
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by School of Environment, Education and Development
Available as a free choice unit? Yes

Overview

This course unit aims to provide an understanding of data that is captured through social media, educational technologies and developing technologies exploiting the Internet of Things, including data from assessment, to location to health. It will explore the implications, for education and learning, of the ways in which data is acquired, explored and manipulated. Students will explore critical approaches to capturing data for education and learning purposes and to current technical approaches in ‘Big data’, data science and learning analytics in education. They will develop understandings of themes and concepts central to data use and management in learning and learning technology and will foster skills in critically evaluating data related issues in educational policy, teaching and learning. Students will explore ‘future-spectives’ on data in education, critically engaging in debates such as, how is bias embedded in software/algorithms, does data and code represent the world ‘as it is’, is decision making in education increasingly data driven and does this matter?

 

 

Aims

This course unit aims to provide an understanding of data that is captured through social media, educational technologies and developing technologies exploiting the Internet of Things, including data from assessment, to location to health. It will explore the implications, for education and learning, of the ways in which data is acquired, explored and manipulated. Students will explore critical approaches to capturing data for education and learning purposes and to current technical approaches in ‘Big data’, data science and learning analytics in education. They will develop understandings of themes and concepts central to data use and management in learning and learning technology and will foster skills in critically evaluating data related issues in educational policy, teaching and learning. Students will explore ‘future-spectives’ on data in education, critically engaging in debates such as, how is bias embedded in software/algorithms, does data and code represent the world ‘as it is’, is decision making in education increasingly data driven and does this matter?

 

 

 

 

 

Teaching and learning methods

 

The core of the course unit will be two five week ‘provocation’ blocks exploring: data capture; and data analytics. Each block will centre on a number of case studies around the capture/analytics themes respectively. The lectures will include practical activities using tools related to the case study: design for mobile apps; basic data mining tools. Students will need to bring their own laptops or work with a peer on these activities. A number of software tools will be accessed in these activities.

 

In each block, one session will allow students to work in small groups to develop and demonstrate their understanding of these tools in a design or activity of their own, which they will then present and debate with their peers in the final session of each block. All activity will be supported by online activities and discussion boards. Distance students will work on activities individually, but presentations and tutorials will take place via synchronous online sessions, lectures will take the form of short videos and practical workshops will utilise screen capture tutorials.

Knowledge and understanding

 

.Students will become familiar with underpinning concepts around data in education and learning. They will explore competing influences stemming from cognitive, behaviorist principles and those from social theory perspectives: they will understand how the design and analytical approaches influencing current technology developments and policy draw on existing theories of learning, and how data driven decisions in policy and technology in turn reinforce and privilege certain discourses about learning and education. They will explore social and ethical implications of these implementations and associated discourses for current and future learning environments.

Intellectual skills

 

  • Critically evaluate academic literature and learning resources in terms of design for data capture and use
  • Critically evaluate claims for both potentialities and pitfalls of data acquisition and use in education through technology developments such as the Internet of Things
  • Critically reflect on their own practices with technology for learning and education in terms of the data they provide, security and privacy issues

Practical skills

 

 

  • Basic skills with technologies for application design
  • Introductory data mining skills
  • Presentation skills
  • Design skills for learning technologies

Transferable skills and personal qualities

 

  • An overview of current debates around data, design for and analysis of data and implications for education and learning
  • Exploration of case studies on how data is gathered, including humane design, and datafication of children in education
  • Exploration of case studies on how data is used, including algorithmic bias and learning analytics
  • An insight into current technology developments in data acquisition and analysis, including Internet of Things and machine learning
  • Experience simple data mining tools and machine learning platforms
  • Ethical and social implications of perspectives embedded in technology design and use including security, privacy and associated debates on regulation and openness

 

Assessment methods

Method Weight
Report 50%
Oral assessment/presentation 50%

Feedback methods

Feedback will be available on Blackboard

Recommended reading

  • Buolamwini, J (n.d.) Algorithmic Justice League: Unmasking Bias. Retrieved from: https://filmmakerscollab.networkforgood.com/projects/26497-filmmakers-collaborative-current-projects-algorithmic-justice-league
  •  Buolamwini, J. and Gebru, T. (2018) Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research 81:1–15
  •  Domingos, P. (2015) The Master Algorithm: How the quest for the ultimate learning machine will remake our world. Penguin: UK.
  •  Eubanks, V. (2017) Automating Inequality. St. Martin’s Press: New York.
  •  Eyal, N. (2014). Hooked: How to build habit-forming products.
  •  O’Neil, C. (2016) Weapons of Math Destruction. Penguin, UK
  •  Watters, A. (2014) The Monsters of Education Technology. Retrieved from Amazon.co.uk and at: http://monsters.hackeducation.com/
  •  Thaler, R. H. and C. R. Sunstein. (2009). Nudge: Improving Decisions About Health, Wealth, and Happiness. New York: Penguin Books
  •  Williamson, B. (Ed.). (2015). Coding/Learning: software and digital data in education. Stirling: University of Stirling.
  •  Williamson, B. (2017) Big data in Education: The digital future of learning, policy and practice.  Sage: London.
  •  Zang J, Dummit K, Graves J, Lisker P, Sweeney L. (2015). Who Knows What About Me? A Survey of Behind the Scenes Personal Data Sharing to Third Parties by Mobile Apps. Technology Science. Retrieved from: https://techscience.org/a/2015103001
 
 

Study hours

Scheduled activity hours
Practical classes & workshops 150
Independent study hours
Independent study 0

Teaching staff

Staff member Role
Amanda Banks Gatenby Unit coordinator
Heather Nicholls Unit coordinator

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