learning analytics - a new discipline and linked data
DESCRIPTION
TRANSCRIPT
Learning Analytics - A New Discipline and Linked Data -
Dragan Gašević @dgasevic
https://semtech.athabascau.ca
http://
Today’s idea of “learning”
Topics to discuss
Learning – today, tomorrow…
Learning analytics – definition
Learning analytics – approaches & challenges with bits of semantics
Part I
Learning Today, Tomorrow
Learning Paths
http://www.w3.org/2006/Talks/1023-sb-W3CTechSemWeb/DataServicesWebAppMetro2.jpg
Educational Ecosystems
Authoring
Reusability
Packaging
Educators
Reusability Adaptivity Evolution Collaboration with educators and students
Educational Ecosystems
Authoring
Reusability
Packaging
Educators
Feedback
Learning and Collaborating
Personalization Adaptivity Context-awareness Social interaction …
Learners
Educational Ecosystems
Authoring
Reusability
Packaging Learning and Collaborating
Community
Peer-Review
Presenting
Administration
Mobile
Educators Learners
Learning (Life) not in a box!
A small part of a software engineer’s life
Learning ecosystem
…
…
…
… …
A small part of a software engineer’s life
Three Generation of Distance Education Pedagogies
Anderson, T. & Dron, J. (2011) Three Generations of Distance Education Pedagogy, International Review of Research in Open and Distance Learning 12(3), 80-97, http://goo.gl/j3mRF
Part II
Learning Analytics
Learning Analytics – What?
Measurement, collection, analysis, and reporting of data
about learners and their contexts
Learning Analytics – Why?
Understanding and optimising learning and the environments
in which learning occurs
http://solaresearch.org/OpenLearningAnalytics.pdf
Evidence-based Education
As the integration of best research evidence with practitioner expertise and stakeholder values
The goal made up based on
Part III
Learning Analytics Approaches and Challenges
- with bits of semantics -
Learning Analytics
What and how to collect?
Measurement, collection, analysis, and reporting of data about learners and their contexts
Ubiquitous learning analytics
Tool and format independent
Aggregates and integrates
Semantic Web
Ontologies: Interconnecting applications
Shared domain conceptualizations
Linked Data
http://richard.cyganiak.de/2007/10/lod/
Linked Data
http://richard.cyganiak.de/2007/10/lod/
“A crazy problem requires a crazy solution!”
(Griff Richards, 2005)
Learning Context Ontology: LOCO
Jovanovic, J., Knight, C., Gasevic, D., Richards, G., "Ontologies for Effective Use of Context in e-Learning Settings," Educational Technology & Society, Vol. 10, No. 3, 2007, pp. 47-59
LOCO-Analyst
OAST and LOCO-Analyst
Ali, L., Hatala, M. Gašević, D., Jovanović, J., "A Qualitative Evaluation of Evolution of a Learning Analytics Tool," Computers & Education, Vol. 58, No. 1, 2012, pp. 470-489, http://goo.gl/lCvMT
LOCO-Analyst
OAST and LOCO-Analyst
Ali, L., Hatala, M. Gašević, D., Jovanović, J., "A Qualitative Evaluation of Evolution of a Learning Analytics Tool," Computers & Education, Vol. 58, No. 1, 2012, pp. 470-489, http://goo.gl/lCvMT
LOCO-Analyst
Formative Evaluation
Category Sub-category Q8
Suggestions for improving
Visualization/GUI 77.8%
Annotations 5.66%
Other Features 11.11%
No suggestions but liked
Data Visualization -
Interface Design 5.56%
Annotations -
Ali, L., Hatala, M. Gašević, D., Jovanović, J. (2012). A Qualitative Evaluation of Evolution of a Learning Analytics Tool. Computers & Education, 58(1) 470-489, http://goo.gl/lCvMT
Learning Analytics
What and how to report?
Measurement, collection, analysis, and reporting of data about learners and their contexts
Visual learning analytics
Ubiquitous LA impossible w/o visual
Information overload
LOCO-Analyst
LOCO-Analyst
LOCO-Analyst
LOCO-Analyst
Student comprehension
Student comprehension
Learning Analytics Acceptance Model
Inspired by Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D., 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly, (27:3), pp. 425-478.
Learning Analytics Acceptance Model
Ali, L., Asadi, M., Jovanović, J., Gašević, D., Hatala, M., “Factors influencing Perceived Utility and Adoption of a Learning Analytics Tool: An Empirical Study,” Computers & Education (submitted)
Learning Analytics Acceptance Model
Learning Analytics Acceptance Model
Learning Analytics
What to measure?
Measurement, collection, analysis, and reporting of data about learners and their contexts
Learning Analytics for Community of Inquiry
Effects of instructional interventions
Example: Role playing (invited expert and moderation) with explicit instructions how to contribute
Social Network Analytics in the Community of Inquiry
Just information sharing does not mean a central role
Social Network Analytics
Performance prediction based on joint course enrollment
Example: Degree, between centrality and closeness centrality explain ~46% of GPA
http://learningworksforkids.com/EF/metacognition.html
Social Learning Analytics for Self-regulated Workplace Learning
Siadaty, M., Gašević, D., Jovanović, J., Milikić, N., Jeremić, N., Ali, L., Giljanović, A., Hatala, M., "Learn-B: Social Analytics-enabled Tool for Self-regulated Workplace Learning," In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 2012, http://goo.gl/Vm8tv
Social Learning Analytics for Self-regulated Workplace Learning
Siadaty, M., Gašević, D., Jovanović, J., Milikić, N., Jeremić, N., Ali, L., Giljanović, A., Hatala, M., "Learn-B: Social Analytics-enabled Tool for Self-regulated Workplace Learning," In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 2012, http://goo.gl/Vm8tv
Social Learning Analytics for Self-regulated Workplace Learning
Siadaty, M., Gašević, D., Jovanović, J., Milikić, N., Jeremić, N., Ali, L., Giljanović, A., Hatala, M., "Learn-B: Social Analytics-enabled Tool for Self-regulated Workplace Learning," In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 2012, http://goo.gl/Vm8tv
DEPTHS
DEsign Patterns Teaching Help System
Project-based learning
Self-regulation and community of inquiry
http://op4l.fon.bg.ac.rs/op4l_services
DEPTHS
LOD-based Personal Learning Environments: Principles
Principle 1:
Integration
Integration of distributed and heterogeneous data sources, tools and services Quintessential for the realization of all other principles, and thus development of advanced PLEs, i.e., PLEs
offering context-aware and personalized learning, as well as ubiquitous data access
Principle 2: Openness
Open standards => application and device independence, long-term access to content and services,
interoperability Open source software => cost-effective customizations to the users’ needs,
Open content => more diverse and constantly evolving and improving educational content
Principle 3: Distributed Identity
Management
The users’ ability to:
- seamlessly access different tools/services that are part of their PLEs;
- pull together their profile data from those tools/services;
- regulate the use of their data within tools/services that from their PLEs.
Principle 4:
Context-awareness
Improved efficiency of user’s interactions with the environment through capturing and leveraging data about
the user's learning context;
Improvements: higher quality of search results, proactive recommendations, mediation of
communication/collaboration
Principle 5: Modularity The ability to seamlessly “configure” a PLE for any given purpose (i.e., learning goal), by adding new and/or replacing existing content, tools and/or services
Support for standardized and light-weight approaches for the development of dynamic (e-learning) mashups.
Principle 6: Ubiquitous
data access
Seamless access to and integration of profile data, data about learning activities and learning resources
Ability to access and use relevant resources regardless of the system/tool/service the user is currently using
Principle 7:
User Centricity
The ‘user at the centre’ paradigm – student is responsible for managing his/her individual knowledge and competences
The learning system is the facilitator: it identifies the appropriate resources, adapts them to the user’s learning
context, and suggests the most appropriate learning strategies
Jeremić, Z., Jovanović, J., Gašević, D., "Personal Learning Environments on the Social Semantic Web," Semantic Web Journal, 2012 (in press), http://goo.gl/yaqQN
Learning Analytics
How to analyze?
Measurement, collection, analysis, and reporting of data about learners and their contexts
Measuring Cognitive Presence
Text mining and linked data
A very similar text-mining problem is spam classification.
Sure, sounds funny, but computing is a strange affair !
http://www.cs.waikato.ac.nz/ml/weka
Cognition and meta-cognition
Discovering learning processes
http://www.processmining.org
http://goo.gl/jtO3i
SNAPP
http://nodexl.codeplex.com/
http://linkededucation.org
Linked Learning 2012 @ WWW 2012 http://lile2012.linkededucation.org/
Learning analytics
more
meaningful & ubiquitous
with semantic technologies
Thank you!