This position paper describes a long-term Technology-Enhanced Learning ini-tiative at the Leeds Institute of Medical Education in which a personalised adaptive learning mentor will be deployed for all MBChB students enrolled in the course. The system, myPAL, is enriching the existing TEL programs em-bedded in the curriculum and will be leveraging recent advances in Learning Analytics and Open Learner Modelling. The paper presents the context of the project and the opportunities that deployment settings will offer, and highlights the research and development strands that will underpin it.

1. Personalised and Adaptive
Mentoring in Medical Education
The myPAL project
Nicolas Van Labeke – Jane Kirby – Trudie Roberts
Leeds Institute of Medical Education, Leeds (UK)
n.vanlabeke@leeds.ac.uk
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First International Workshop on Intelligent Mentoring
Systems (IMS 2016)
ITS’2016 – Zagreb

2. Leeds MBChB
Bachelor of Medicine and Bachelor of
Surgery
Undergraduate studies
5 years medical program
[ plus intercalated year + foundation
years FY1/FY2 + lifelong learning ]
250 students a year
Combination of formal academic learning
and (increasingly) placement and
workplace
Curriculum-based (“spiral” model)
“pass or fail” marking, collaboration-
driven
07/06/2016 NICOLAS VAN LABEKE @ IMS 2016 (ITS'16, ZAGREB) 2

3. Medical Education – Entrustability Scale
observe-supervise-initiate-peer teach
07/06/2016 NICOLAS VAN LABEKE @ IMS 2016 (ITS'16, ZAGREB) 3
Experience
-> Ability
-> Expectations

4. Summative
Evaluation
Competency-based assessment
methodology
Performance objectives
Curriculum
Objectivity and quality
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http://www.slideshare.net/ThomasKropmans/qsapple2015-back-to-the-future-
electronic-marking-of-objective-structured-clinical-examinations-and-admission-
interviews-for-the-higher-education-market

5. LIME / TIME
Technology in Medical Education
07/06/2016 NICOLAS VAN LABEKE @ IMS 2016 (ITS'16, ZAGREB) 5

6. myPAL – Interventions
in real-life settings
Personalised Adaptive Learning
◦ Learning Analytics (predictive models, SNA)
◦ Open Learner Modelling (self-reflection, motivation)
◦ Technology Enhanced Learning (deeper learning)
Challenges:
◦ practice, performance and learning are so interlinked they
are inseparable and dependent on the specific setting.”
(Kilminster et al. 2011)
◦ Placement included in many training settings but
tutoring/mentoring not cheap and opportunity for
feedback very limited (time, responsibility, training)
◦ Mix of formal/informal settings, summative and formative
data [ AND a lot of unexploited/uncollected data! ]
◦ Co-design with students over the 5 years of curriculum
◦ Technology-Enhanced: mobile learning, wearables, social
sensing, intelligent mentoring
◦ Ethics, Privacy and Consent (data ownership)
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7. myPAL
Design-Based Research
NICOLAS VAN LABEKE @ IMS 2016 (ITS'16, ZAGREB)07/06/2016 7

8. Toward Intelligent Mentoring Systems?
MBChB & medical education (and lifelong learning)
Traditional TEL approaches have a role to play
◦ Digital resources, eLearning, Simulation-based Learning, ITSs
IMS : Transition from academic to workplace, practice-based learning
◦ Self-regulated learning
◦ Professionalism and developmental models (competency, proficiency, …)
Repurposing or designing new socio-technical approaches
◦ Semantic augmentation for legacy TEL systems
◦ Practice-based learning analytics
◦ Implicit/explicit feedback mechanism for “timely” delivery
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9. Reflections and Questions
Relevance and role of Learning Analytics?
◦ Where is learning in work context?
◦ What data streams and how do we capture it?
Learning = action + feedback + reflection +
action (feedforward)
Technological solutions
◦ What predictive & pedagogical model(s)?
◦ Domain-specific or classroom-to-practice
transitions (meta-cognition, inter/intra personal
skills, …)
◦ Automated adaptive feedback?
◦ Operationalisable competence frameworks,
ontologies and semantically augmented data
Dashboard: for whom?
◦ Learner? Tutor? Institution?
◦ Complexity overwhelming? Heavy-handed for
time-strapped learner?
◦ Adaptive, unobtrusive user interface
Sensing, feedback and nudging
◦ Dimitrova, Poulovasilis, Van Labeke et al. (2016).
Intelligent Mentoring Systems for Making
Meaning from Work Experience. Intelligent
Mentoring Systems Worskhop @ ITS’16.
VAN LABEKE ET AL., LEARNING ANALYTICS FOR WORKPLACE AND PROFESSIONAL LEARNING (LA FOR
WORK) @ LAK'16, 25/04/16

10. Learning Analytics – 4 Challenges (2012)
Ferguson, R. (2012). Learning analytics: drivers,
developments and challenges. International
Journal of Technology Enhanced Learning 4(5–
6), pp. 304–317.
◦ Build strong connections with the learning
sciences
◦ How learning takes place, can be supported; importance of
factors such as identity, reputation and affect; increasing
student awareness
◦ Develop methods of working with a wide range of
databases in order to optimize learning
environments
◦ Analytics outside VLE/LMS, shift toward open and informal
settings; mobile and biometrics data
◦ Focus on the perspectives of learners
◦ Extend criteria for learning success beyond grade and
persistence, include motivation, confidence, enjoyment,
satisfaction and career goal; moving away from summative
assessment; personalized visualization, transparent
analytics, feedback for refining analytics
◦ Develop and apply a clear set of ethical guidelines
◦ Ownership and stewardship of data; open data & personal
data stores (PDS); consent
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