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Dimensions of personalisation in TEL: a framework and implications for design

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Presentation made as part of the OU's CALRG special extended session on personalisation.

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Dimensions of personalisation in TEL: a framework and implications for design

  1. 1. Dimensions of personalisation in technology-enhanced learning: A framework and implications for design Elizabeth FitzGerald LTI-Academic/IET
  2. 2. Introduction • Personalisation is a recurring trend in education, with a number of programmes designed to deliver learner-centred education. • Personalisation can occur in a number of different ways, both in technology- enhanced learning (TEL) and non-TEL environments. • Personalisation lends itself well to TEL environments but there are no design guidelines, frameworks or tools to guide its implementation and hence practice can vary widely.
  3. 3. Key aspects of non-TEL personalisation • Much literature in this area – usually formal/compulsory education, classroom- based • UK govt (DfES) in 2004 suggested 5 aspects: – assessment for learning; teaching and learning strategies; curriculum entitlement and choice; a student-centred approach to school organisation; and a strong partnership beyond the school • Hargreaves suggested 4 more: – new technologies; workforce development; advice and guidance; and mentoring and coaching
  4. 4. Not much about TEL personalisation though… • Martinez, 2002: five components of personalisation achieved through TEL, in levels of increasing sophistication: – name recognition; – self-described personalisation; – segmented personalisation; – cognitive-based personalisation; and – whole-person personalisation • Fairly limited model in considering the wide range of factors that impact upon learning and only really considers the individual
  5. 5. Process of identifying dimensions • Grounded approach: careful scrutiny and conceptual analysis of the published literature of personalisation in TEL in the last decade • Independently classified the different aspects found, before discussing the overlap • Heuristic and iterative development of the dimensions with multiple cycles to ensure full coverage of the emerging themes and to ascertain full analysis of the literature/projects examined
  6. 6. Dimensions of TEL personalisation • What is being personalised • The type of learning where personalisation occurs • What personal characteristics of the learner may be addressed • Who/what is doing the personalisation • How is personalisation carried out • The impact/beneficiaries of the personalisation
  7. 7. Dimension 1: What is being personalised? Dimension 2: Type of learning Dimension 3: Personal characteristics of the learner Dimension 4: Who/what is doing the personalisation Dimension 5: How is personalisation carried out? Dimension 6: Impact/beneficiaries  Content  Assessment  Teaching and learning strategy (e.g. group work; individual; peer learning)  Learner choice (e.g. of resources, topics or mode/approach of study)  Teacher choice (e.g. curriculum choices or ordering of curriculum)  Formal (e.g. compulsory, primary, secondary etc)  Non-formal  Informal  Demographic (e.g. age, cultural background)  Prior knowledge (e.g. based on recent assessment scores)  Self-assessed knowledge (by teacher or learner)  Demonstrated interests or personal relevance (e.g. could feed into learner profile)  Preferred mode of learning e.g. distance, online, evening classes  Level of learner commitment/mot ivation and self- regulation  Learner  Teacher  Peer  Computer software and/or algorithms  Name recognition  Self-described personalisation  Segmented personalisation  Cognitive-based personalisation  Whole-person personalisation (affective elements)  Organisation of resources  Learners  Teachers  Trainers and training providers  School/organisati on  Government (local or national level)  Commercial entities e.g. software developers Table 1: Framework for modelling dimensions of personalisation in TEL
  8. 8. Theoretical basis • Builds mainly on cognitive and constructivist theories of learning based on work by Vygotsky • Emphasis on socio-cultural aspects and need to examine learning from number of different perspectives
  9. 9. Case studies in the paper • Intelligent Tutoring Systems (ITS) and Adaptive Educational Hypermedia (AEH) • Adaptive assessment • Science inquiry learning • Gaming and informal learning • Learning analytics • Personalised books
  10. 10. Example 1: Intelligent Tutoring Systems (ITS) and Adaptive Educational Hypermedia (AEH) • ITS from psychology, AEH from computer science • Learner model/profile as basis for personalisation • Learning environment/VLE is modified to match learner profile: adaptive links (different links), adaptive content or adaptive presentation of resources (e.g. different colour schemes, designs or website navigation) • Ideally system automatically updates in real time to match learner engagement/improvement • In reality, this synchronicity rarely occurs and usually needs manual updating
  11. 11. Dimension 1: What is being personalised? Dimension 2: Type of learning Dimension 3: Personal characteristics of the learner Dimension 4: Who/what is doing the personalisation Dimension 5: How is personalisation carried out? Dimension 6: Impact/beneficiaries Content, navigation, links and visual design Formal Emphasis on prior knowledge (e.g. based on recent assessment scores) Carried out by computer software, sometimes based on information inputted by the learner e.g. response to a questionnaire Tends to be cognitive-based personalisation Learner (most direct impact) but could also be the teacher if savings can be made in terms of time and costs devoted to developing differentiated teaching materials Table 2: Personalisation dimensions in Intelligent Tutoring Systems (ITS) and Adaptive Educational Hypermedia (AEH):
  12. 12. Example 2: Learning analytics • Learning analytics can be used currently to provide personalised feedback and support • Sits at a crossroads between technical and social learning theory fields • Learner affect can help create a more effective model of the learner • Could capture learner data and model their emotions, via facial recognition, processing voice recorded data, sentiment analysis of student comments, and heart rate detection using video cameras, etc. • Exemplified by the ‘whole person’ personalisation element in Dimension 5 and have a high level of sophistication, taking into account a large number of learning characteristics
  13. 13. Dimension 1: What is being personalised? Dimension 2: Type of learning Dimension 3: Personal characteristics of the learner Dimension 4: Who/what is doing the personalisation Dimension 5: How is personalisation carried out? Dimension 6: Impact/beneficiaries Content Assessment Visual design/presentati on of resources Formal Demographic (e.g. age, cultural background) Prior knowledge (e.g. based on recent assessment scores) Self-assessed knowledge (by teacher or learner) Demonstrated interests or personal relevance (e.g. could feed into learner profile) Level of learner commitment/motivation and self-regulation Computer software and/or algorithms Whole person personalisation (affective elements) Learners Teachers Schools/organisations (in terms of large scale learner tracking) Table 3: Personalisation dimensions in learning analytics:
  14. 14. Summary • Initial attempt at mapping the key dimensions of, and providing the language for, personalised TEL approaches. • Future research could expand the facets and levels of our key dimensions (e.g. , micro, meso, macro; or at a learner’s individual level compared to an institutional level). • Useful for variety of stakeholders including: – Developers of educational software, who are being urged to consider how to integrate personalisation into their new solutions – Teachers, in considering how to provide personal support for all their students, particularly in their online activities. • Framework could be used to support teacher professional development, to help them understand how personalisation might occur already and to see how it might be integrated more closely in the future, into their daily activities.
  15. 15. Implications for practice and/or policy • The framework can be used to analyse existing personalisation in TEL and also help guide the design of new implementations. • The article provides a shared understanding of what personalisation is in TEL environments and suggests a useful shared language for onward discussions. • The dimensions in the framework can be used as the basis for future systematic reviews, which may help identify which aspects of personalisation are effective for learning.
  16. 16. Questions/discussion • Thanks for listening – questions, discussion etc? • Full paper reference: FitzGerald, Elizabeth; Kucirkova, Natalia; Jones, Ann; Cross, Simon; Ferguson, Rebecca; Herodotou, Christothea; Hillaire, Garron and Scanlon, Eileen (2017). Dimensions of personalisation in technology-enhanced learning: a framework and implications for design. British Journal of Educational Technology (In press). ORO link || Available to download from ResearchGate || Official BJET link Email: elizabeth.fitzgerald@open.ac.uk Twitter: @elara99 ResearchGate: http://tinyurl.com/RG-EFitzgerald

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