11. 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
23. Linked Data
http://richard.cyganiak.de/2007/10/lod/
24. Linked Data
http://richard.cyganiak.de/2007/10/lod/
25. “A crazy problem requires
a crazy solution!”
(Griff Richards, 2005)
26. 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
27. 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
28. 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
30. Formative Evaluation
Category Sub-category Q8
Visualization/GUI 77.8%
Suggestions for improving Annotations 5.66%
Other Features 11.11%
Data Visualization -
No suggestions but liked 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
31. Learning Analytics
What and how to report?
Measurement, collection, analysis, and reporting of data
about learners and their contexts
39. 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.
40. 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)
43. Learning Analytics
What to measure?
Measurement, collection, analysis, and reporting of data
about learners and their contexts
44.
45. Learning Analytics for
Community of Inquiry
Effects of
instructional interventions
Example: Role playing (invited expert and moderation) with
explicit instructions how to contribute
46. Social Network Analytics in
the Community of Inquiry
Just information sharing
does not mean a central role
47. Social Network Analytics
Performance prediction based
on joint course enrollment
Example: Degree, between centrality and closeness centrality
explain ~46% of GPA
49. 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
50. 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
51. 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
52. DEPTHS
DEsign Patterns Teaching Help System
Project-based learning
Self-regulation and community of inquiry
http://op4l.fon.bg.ac.rs/op4l_services
55. LOD-based Personal Learning
Environments: Principles
Integration of distributed and heterogeneous data sources, tools and services
Principle 1:
Quintessential for the realization of all other principles, and thus development of advanced PLEs, i.e., PLEs
Integration
offering context-aware and personalized learning, as well as ubiquitous data access
Open standards => application and device independence, long-term access to content and services,
interoperability
Principle 2: Openness
Open source software => cost-effective customizations to the users’ needs,
Open content => more diverse and constantly evolving and improving educational content
The users’ ability to:
Principle 3: - seamlessly access different tools/services that are part of their PLEs;
Distributed Identity - pull together their profile data from those tools/services;
Management
- regulate the use of their data within tools/services that from their PLEs.
Improved efficiency of user’s interactions with the environment through capturing and leveraging data about
Principle 4: the user's learning context;
Context-awareness Improvements: higher quality of search results, proactive recommendations, mediation of
communication/collaboration
The ability to seamlessly “configure” a PLE for any given purpose (i.e., learning goal), by adding new and/or
Principle 5: Modularity 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 Seamless access to and integration of profile data, data about learning activities and learning resources
data access Ability to access and use relevant resources regardless of the system/tool/service the user is currently using
The ‘user at the centre’ paradigm – student is responsible for managing his/her individual knowledge and
Principle 7: competences
User Centricity 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
56. Learning Analytics
How to analyze?
Measurement, collection, analysis, and reporting of data
about learners and their contexts
57. 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 !
