learning analytics. what are learning analytics? related fields of study processesresources...
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learning analytics
what are learning analytics?
related fields of study
processes resources
implementation tips
a model for learning analytics
references
where are we now?
literature review
learning analytics are:
the ability to “scale the real-time use of learning analytics by students, instructors and academic advisors to improve student success”
- Next Generation: Learning Challenges
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next page: learning analytics involves
learning analytics involves:
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1. the development of new processes and tools aimed at improving learning and teaching for individual students and instructors
2. the integration of these tools and processes into the practice of teaching and learning
related links
next page: related fields of study
related fields of study
action analytics
academic analytics
web analytics
business intelligence
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business intelligence: a well-established process in the business world whereby decision makers integrate strategic thinking with information technology to be able to synthesize “vast amounts of data into powerful, decision making capabilities”
- Baker, 2007
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next page: web analytics
web analytics: “the collection, analysis and reporting of Web site usage by visitors and customers of a web site” in order to “better understand the effectiveness of online initiatives and other changes to the web site in an objective, scientific way through experimentation, testing, and measurement”
- McFadden, 2005
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next page: academic analytics
academic analytics: the application of the principles and tools of business intelligence to how institutions gather, analyze, and use data to improve student success
-Campbell and Oblinger, 2007 &Goldstein and Katz, 2005
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next page: action analytics
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action analytics: involves deploying academic analytics “ to provide actionable intelligence, service-oriented architectures, mash-ups of information/content and services. proven models of course/curriculum reinvention, and changes in faculty practice that improve performance and reduce costs
- Norris et al, 2008
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learning analytics processes
data gathering
knowledge application
information
processing
aggregate
aggregate
capture
capture
selectselect
predictpredictuseuse
refinerefine
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Large store of data already exist and computer-mediated distance education increasingly creates student data trails.
Most often exists in disjointed and meaningless forms.
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next page: information processing
data gathering
capturecapture
selectselect
There are so many metrics that could be tracked, it is essential to define goals and identify relevant data. What do we want to achieve? Are we measuring what we should be? How can we create innovative metrics?
To be usable, we must be able to aggregate that data into a meaningful form.
Dashboards and social network analysis are two promising tools.
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next page: information processing
information processing
aggregateaggregatepredictpredict
Data is useful when it can be used to predict future events.
To date, however, no guidance it available to educators to indicate which captured variables are pedagogically meaningful.
Outside of education, search engines and recommenders sites are examples of aggregating information and using it to predict user needs.
In order to be a knowledge discovery cycle, data and actions must be re-presented to users. Otherwise, it is just data mining.
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next page: analytics tools
knowledge application
useuse
refinerefine
Analytics are a self-improvement project. Monitoring impact must be a continual effort, the results of which are used to update the models and improve predictions.
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next page: analytics tools
When institutions work together and share, duplication is reduced and improvements are increased.
Sharing data, models and innovations, therefore, has the potential to improve learning for everyone.
learning analytics resources
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There are four types tools that must interact for learning
analytics to be successful.
...a single amalgam of human and
machine processing which
is instantiated through an
interface that both drives and is
driven by the whole system,
human and Machine
- Dron and Anderson, 2009
Sophisticated computers already collect data.
They also facilitate data processing with visualization tools because we can process an incredible amount of information if it is packaged and presented correctly.
Two promising visualization tools for learning analytics are dashboards and social networks maps.
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next page: dashboards
computers
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dashboards
Meaningful information can be can be extracted from CMS/LMS and be made available to students and instructors.
next page: social network analysis
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social network maps
next page: theory
Automates the process of extraction, collation,
evaluation and visualisation of student network data
into a form quickly usable by instructors.
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Computer hardware and software are only useful if they are based on sound theory.
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next page: people
theory
Social networks maps, for example, are only useful because of sound research-based theory that demonstrates we learn better when we interact with others.
There are still a significant aspects of an analytics system that require human knowledge, skills and abilities to operate.
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next page: organizations
people
Developing effective learning interventions remains highly dependent on human cognitive problem-solving and decision-making skills.
Social networks maps, for example, are only useful because of sound research-based theory that shows peer networks play an important role in student persistence and overall success.
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next page: organizations
organizations
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knowledge application
information processing
aggregateaggregate
predictpredictuseuse
data gathering
a model for learning analytics capturecapture
selectselect
refinerefine
where are we now?Learning analytics is an emerging field.
Analytics is other fields
is already well
established.
Tools and lessons learned from other fields can be used to support the introduction of learning analytics to the majority.
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next page: tips for analytics
more information
implementation tips 1. Learn from others disciplines in which analytics
is an established field 2. Find out what you are already measuring3. Combine web-based data with traditional
evaluation, assessment and demographic data4. Good communication skills are essential5. Change is hard for everyone and rarely
welcome - tread lightly and offer support
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next page: references
references Arnold, K. E. (2010). Signals: Applying Academic Analytics, EDUCAUSE Quarterly 33(1). Retrieved October 1, 2010 from http://www.educause.edu/EDUCAUSE+Quarterly/EDUCAUSEQuarterlyMagazineVolum/SignalsApplyingAcademicAnalyti/199385
Astin, A. (1993). What Matters in College? Four Critical Years Revisited. San Francisco: Jossey-Bass.
Baker, B. (2007). A conceptual framework for making knowledge actionable through capital formation. D.Mgt. dissertation, University of Maryland University College, United States -- Maryland. Retrieved October 19, 2010, from ABI/INFORM Global.(Publication No. AAT 3254328).
Dron, J. and Anderson, T. (2009). On the design of collective applications, Proceedings of the 2009 International Conference on Computational Science and Engineering , Volume 04, pp. 368-374.
Goldstein, P. J. and Katz, R. N. (2005). Academic Analytics: The Uses of Management Information and Technology in Higher Education, ECAR Research Study Volume 8. Retrieved October 1, 2010 from http://www.educause.edu/ers0508
next page: references (cont’d)
references (continued)
McFadden, C. (2005). Optimizing the Online Business Channel with Web Analytics [blog post]. Retrieved October 5, 2010 from http://www.webanalyticsassociation.org/members/blog_view.asp?id=533997&post=89328&hhSearchTerms=definition+and+of+and+web+and+analytics
NextGeneration: Learning Challenges (n.d.). Learning Analytics [website]. Retrieved October 12, 2010 from http://nextgenlearning.com/the-challenges/learning-analytics
Norris, D., Baer, L., Leonard, J., Pugliese, L. and Lefrere, P. (2008). Action Analytics: Measuring and Improving Performance That Matters in Higher Education, EDUCAUSE Review 43(1). Retrieved October 1, 2010 from http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume43/ActionAnalyticsMeasuringandImp/162422
Zhang, H. and Almeroth, K. (2010). Moodog: Tracking Student Activity in Online Course Management Systems. Journal of Interactive Learning Research, 21(3), 407-429. Chesapeake, VA: AACE. Retrieved October 5, 2010 from http://0-www.editlib.org.aupac.lib.athabascau.ca/p/32307.