MA Digital Technologies, Communication and Education / Course details

Year of entry: 2022

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Course unit details:
AI Perspectives on Learning

Course unit fact file
Unit code EDUC71242
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by School of Environment, Education and Development
Available as a free choice unit? Yes

Overview

 

  

  • an overview of current technological innovations in computing from AI to biological and quantum computing and the implications for learning and education;
  • basic principles of programming, coding and digital making pedagogy;
  • using a range of technologies for learning and expressing computationally, digital making and developing tools and games for learning, for example, Scratch, Greenfoot, Bubble, Codebug and chatbots;
  • AI theories for learning, including machine learning, neural networks and robotics;
  • the historical development of AI and the theories of learning and language that have informed and emerged from AI;
  • current AI research and connections with learning theories including play and gamification;
  • ethical and social implications of these perspectives including issues of gender, sustainability and ‘scientism’
  • ;methodological approaches to evaluating learning such as TAP (Think Aloud Protocol) and process-based assessment

Aims

   

Artificial Intelligence is the development and implementation of computational models of human intelligence, learning and knowledge transfer, informed by theories of mind and consciousness. This course unit aims to provide a broad exploration of these computational perspectives on learning and the principles of computation that underpin learning technologies, to develop students’ ability to critically evaluate and design for learning with technology that is changing at an accelerating rate. Students will develop ‘AI literacy’ by learning to read and interrogate the code that drives technologies such as chatbots and robots. We will explore research in emerging fields around developmental robotics and ‘teacher-bots’, as well as considering the implications of AI technology for existing technologies such as VR/AR, how AI is already changing these and what this might mean for education and learning. Students will learn to read code through playing and ‘digital making’ with code. Introduction to basic programming skills for building digital artefacts for learning will enable students to understand and contribute to debates on how education might develop creators rather than consumers of technology. No prior knowledge of computer programming is required and this is not a computer science unit: programming work is based on activities aimed at complete beginners, some of which is aimed at primary years. The focus is on conceptual aspects of computing and understanding cutting edge developments that are changing the technologies we use now and the ethical implications for learning and education. AI draws on all the learning sciences from neuroscience, through cognitive science and psychology to social learning theories and this unit will use this transdisciplinary perspective to broaden and challenge conceptions of what it means to learn.

Learning outcomes

   

The structure of the course will be a weekly lecture to discuss context and theory with associated digital maker-space lab: hands on supported experience with a range of AI and digital making tools, including those designed to teach basic coding, physical computing and app development through user journey design. Lectures will involve some elements of flipped teaching where students are expected to prepare material prior to f2f sessions. Blackboard will be used to provide access and structure to the resources for preparation. Lectures will be predominantly interactive, with techniques for enabling contribution from all students, such as back channels, embedded into the activities to support formative assessment. Teaching will be adjusted according to learner needs identified through this communication. Distance students will listen to podcasts and have 3 synchronous sessions. Maker-space sessions will be hands on work in a computer lab or for distance students, synchronous sessions with screen share software where appropriate. All activities will involve collaboration through a combination of group work and peer support, and through connections with students in other schools within the university.

 

Teaching and learning methods

 

The structure of the course will be a weekly lecture to discuss context and theory with associated digital maker-space lab: hands on supported experience with a range of digital making tools, including those designed to teach basic coding, physical computing and app development through user journey design. Lectures will involve some elements of flipped teaching where students are expected to prepare material prior to f2f sessions. Blackboard will be used to provide access and structure to the resources for preparation. Lectures will be predominantly interactive, with techniques for enabling contribution from all students, such as back channels, embedded into the activities to support formative assessment. Teaching will be adjusted according to learner needs identified through this communication. Distance students will listen to podcasts and have 3 synchronous sessions. Maker-space sessions will be hands on work in a computer lab or for distance students, synchronous sessions with screen share software. All activities will involve collaboration through a combination of group work and peer support and assessment, and through connections with students in other schools within the university.

Knowledge and understanding

 

  

Students will become familiar with key computationally informed theories of learning and mind and be introduced to principles underpinning learning technologies. They will understand how these theories are situated with other learning theories discovered through the course as a whole, and consider the strengths and limitations of these alternative perspectives for evaluation and design of learning experiences. They will understand social and ethical implications of these theories and the practices of digital making and computation. Frameworks, concepts and methodologies will include:

  • Constructionism and technocentrism
  • Complexity theory
  • Connectionism (neural nets)
  • Gamification
  • Principles of machine learning algorithms
  • UX and data structure principles
  • Discourse analysis, concept mapping stories and TAP (Think Aloud Protocol)

 

Intellectual skills

 

 

  • Discuss and critically analyse the literature and develop a critical approach to different theories of learning
  •  Through use of computational theories of learning, they will develop the ability to understand strengths and limitations of different theoretical perspectives in evaluating learning design

 

Practical skills

 

  • Introductory programming and UX skills
  • Basic skills with technologies for app development
  • Basic skills with technologies for teaching Computing
  • Introduction to and application of additional research methods

Transferable skills and personal qualities

 

  • Further develop reflective practice on their own learning and their design of learning for others
  • Develop technical understanding to enhance communication with technical specialists
  • Organise, manage and leverage own and others time and resources and connecting with digital making communities locally and globally to support future learning

Assessment methods

Method Weight
Other 40%
Written assignment (inc essay) 60%

Feedback methods

Feedback will be available on Blackboard

Recommended reading

 

 

 

  • Brown-Martin, G. (2014) Learning Reimagined, Bloomsbury Academic, UK
  • Cooper, S. B. and Hodges, A. (eds) (2016) The Once and Future Turing, Cambridge University Press, UK
  • DiSessa, A. (2000) Changing Minds: Computers, Learning and Literacy, The MIT Press, Cambridge, Massachusetts
  • Gee, J. P. (2008) What Video Games Have to Teach us About Learning and Literacy, Palgrave Macmillan
  • Minsky, M. (1988) The Society of Mind, Simon & Schuster, New York
  • Papert, S. (1980) Mindstorms: Children, Computers and Powerful Ideas, Basic Books, New York
  • Resnick, M. (2017) Lifelong Kindergarten: Cultivating Creativity Through Projects, Passion, Peers and Play, MIT Press, Cambridge, Massachusetts
  • Wenger, E. (1987) Artificial Intelligence and Tutoring Systems, Morgan Kaufmann, California

 

 

Teaching staff

Staff member Role
Amanda Banks Gatenby Unit coordinator

Additional notes

 

LEARNING HOURS

 

 

 

ACTIVITY

 

HOURS ALLOCATED

Staff/student contact

 

24 (including 12 of tutorial maker space)

Tutorials

 

10 (open maker-space – supported but self-directed IT lab time that can be used to develop assessment work)

Private study

 

70 (including collaborative based assessment preparation)

Directed reading

 

24 + 22 preparation for flipped sessions

Other activities 

eg Practical/laboratory work

 

Included above – tutorials and staff contact time will also involve practical elements as it is of the essence of this course not to divide them

Total hours

 

150

 

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