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Manolis Mavrikis UCL Institute of Education, University College London Learning Analytics for Exploratory and Experiential Learning Potentials and Pitfalls

Learning Analytics for Exploratory and Experiential Learning - Potential and Pitfalls

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Page 1: Learning Analytics for Exploratory and Experiential Learning - Potential and Pitfalls

Manolis Mavrikis UCL Institute of Education, University College London

Learning Analytics for

Exploratory and Experiential Learning

Potentials and Pitfalls

Page 2: Learning Analytics for Exploratory and Experiential Learning - Potential and Pitfalls

Support for Exploratory and Experiential Learning

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Page 3: Learning Analytics for Exploratory and Experiential Learning - Potential and Pitfalls

EXAMPLES FROM EU FP7 PROJECTS

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Karkalas, S., & Gutierrez-Santos, S. (2014). Enhanced JavaScript Learning using Code Quality Tools and a Rule-based System in the FLIP Exploratory Learning Environment. In IEEE ICALT

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Similarities in practice-based learning

across 3 contexts

• Flexible project choice– Student-led

– authentic discipline-related questions

• Collaborative

• Support student self-assessment

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- Electronics kit- Embedded electronics- Camera and motion sensing- Software- Server

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What can LA offer?

• Support the teacher/facilitator

• Increase learner awareness and encourage reflection– Learning how to learn (together)

– Learning how to make

• Help address research questions

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Classroom dynamics

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Gutierrez-Santos, S., Mavrikis, M., Geraniou E., Poulovassilis, A. (2012). Usage Scenarios and Evaluation of Teacher Assistance Tools for Exploratory Learning Environments (Under review) Available at http://www.dcs.bbk.ac.uk/research/techreps/2012/bbkcs-12-02.pdf

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Goal achievement

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Educational Data Mining and Visualisations

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Mavrikis, M., Gutierrez Santos, S., Poulovassilis, A., Zhu, Z. (2014) Indicator Visualization for Adaptive Exploratory Learning Environments. EDM 2014

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Bayesian networks from data

degree of goal

completeness

time in

related pagestime spent

on attempts

need for help

previous

performancedifficulty

features

prediction

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M. Mavrikis (2010) Modelling Student Interactions in Intelligent Learning Environments: Constructing Bayesian Networks from Data. In International Journal on Artificial Intelligence Tools (IJAIT), 19(6):733--753.

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Teacher Visualization

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Barriers to achieving LA potential

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Literacy on decision-making with LA

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The EU projects mentioned here have received funding from the European’s Seventh Framework Programme

Acknowledgements

Sokratis Karkalas

PELARS (UCL-IOE)

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Alice Hansen Wayne HolmesiTalk2Learn

Daniel Spikol(Malmo)

Lorna Stokes (ENOLL)

Emanuele Ruffaldi(SSSA)

David Cuartielles(Arduino)

KaterinaAvramides

MutluCukurova

Rose Luckin

LKL (UCL –IOE)

LKL –(Birkbeck)

Sergio Gutierrez Alex Poulovassilis

MinaVasalou