Learning Analytics - A New Discipline and Linked Data

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

Authoring

Reusability

Packaging

Educators

Feedback

Learning and Collaborating

Personalization Adaptivity Context-awareness Social interaction …

Learners

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?

Visual learning analytics

Ubiquitous LA impossible w/o visual

Information overload

LOCO-Analyst

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.

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

What to measure?

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

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?

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

http://nodexl.codeplex.com/

http://linkededucation.org

Linked Learning 2012 @ WWW 2012 http://lile2012.linkededucation.org/

Learning analytics

meaningful & ubiquitous

with semantic technologies

Thank you!