This document presents a framework for understanding the dimensions of personalization in technology-enhanced learning (TEL). The framework identifies six key dimensions: 1) what is being personalized, 2) the type of learning, 3) personal learner characteristics, 4) who or what is doing the personalization, 5) how personalization is carried out, and 6) the impact and beneficiaries. The framework was developed through analyzing literature on personalization in TEL. Examples of how the dimensions apply to intelligent tutoring systems and learning analytics are provided. The framework is intended to provide a common language for discussing personalization and guide its design and implementation in TEL environments.

1. Dimensions of personalisation in
technology-enhanced learning:
A framework and implications for design
Elizabeth FitzGerald
LTI-Academic/IET

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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