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Week 5: Smarter Curriculum

Week 5: Smarter curriculum: semantic web, linked data, and adaptive content

 

Introduction:

Over the first four weeks of LAK13, we explored the context that has generated interest in learning analytics, the different flavours of analytics (business intelligence, educational data mining, learning analytics, and academic analytics), cases and examples of analytics deployment in higher education, and predictive models. It should be clear at this stage that analytics in education have a diverse heritage and different disciplines are now converging around learning analytics. It is not uncommon to see computer scientists, statisticians, psychologists, graphic designers, and visualization experts involved in an analytics project. Analytics is not yet a domain owned by a particular group of researchers. This is obviously an exciting prospect as there is greater inter-disciplinary discussion occurring in analytics than what is generally found in an established domain.

Learning analytics can be seen as a tiered or staged concept indicating progressive maturity of implementation

  1. Extracting and analyzing data from learning management systems
  2. Building an analytics matrix that incorporates data from multiple sources (social media, LMS, student information systems, etc).
  3. Profile or model development of individual learners (across the analytics matrix)
  4. Predictive analytics: determining at-risk learner
  5. Automated intervention and adaptive analytics: i.e. the learner model should be updated rapidly to reflect near real-time learner success and activity so that decisions are not made on out-dated model
  6. Development of "intelligent curriculum" where learning content is semantically defined
  7. Personalization and adaptation of learning based on intelligent curriculum where content, activities, and social connections can be presented to each learner based on her profile or existing knowledge
  8. Advanced assessment: comparing learner profile with architecture of knowledge in a domain for grading or assessment 

Within the 8 step model detailed above, dashboard, visualizations, and drill down reports are integrated at each level so that educators, learners, and administrators can explore and visualize the data. 

Readings and Videos:


Semantic Web: An Introduction: http://infomesh.net/2001/swintro Links to an external site.

Ray, K (2009) Web 3.0 http://vimeo.com/11529540 Links to an external site.

Berners-Lee, T. (1989) Information Management: A proposal http://www.w3.org/History/1989/proposal.html Links to an external site.

Tim Berners-Lee talk http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html Links to an external site.

Hilary Mason, Machine Learning: http://www.infoq.com/presentations/Machine-Learning Links to an external site.

Jovanović, J., Gašević, D., Brooks, C., Devedžić, V., Hatala, M., Eap, T., Richards, G., "Using Semantic Web Technologies for the Analysis of Learning Content," IEEE Internet Computing, Vol. 11, No. 5, 2007, pp. 45-53, http://goo.gl/eouEW Links to an external site.

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/2gTd4 Links to an external site.

Learning Activities: 

1. Stop by and comment on the assignments submitted by colleagues last week: https://learn.canvas.net/courses/33/discussion_topics/991 

2. Engage in discussion forums for week 5: Week 5 Discussion Forum. In particular, what role do you think "smarter content" will play in the future of education? Are the promises of personalized and adaptive learning overblown?