4. What is Learning Analytics?
The measurement, collection, analysis and reporting of data about
learners and their contexts, for purposes of understanding and optimising
learning and the environments in which it occurs.
1st International Conference on Learning Analytics and Knowledge
An emerging field concerned with analyzing the vast data “given off” by
learners in technology supported settings to inform educational theory
and practice.
Suthers & Verbert, 2013
A field associated with deciphering trends and patterns from educational
big data, or huge sets of student-related data, to further the
advancement of a personalized, supportive system of higher education.
Educause 2013 Higher Ed Report
@cwmlang
#lasi13
9. Buzzwords
MOOCs
Quantified
Self The Cloud Service Oriented
Architecture
Dashboards
Data
Stream
2.0, 3.0,
4.0…
The Next Big
Thing
Mobile
Apps
Dark Data Hack-
Flipped
Classroom
Big Data
Tablet
(Phablet?)
Gamification Portfolio
17. Learning Analytics
Log Parsing Data Collection
Harrer 2013, Analytics of collaborative planning in Metafora -
architecture, data, and analytic methods
18. Learning Analytics Instrumentation
with virtual machines (VMs)
Abelardo 2013, Keynote presentation. Bridging the Middle Space with
Learning Analytics. Keynote presentation at the International
Conference on Learning Analytics and Knowledge, Leuven, Belgium.
19. D’Aquin Jay 2013, Interpreting Data Mining Results with Linked Data for
Learning Analytics: Motivation, Case Study and Directions
Linked Data Analysis
Enriching Data for Learning Analytics
20. Complex Inquiry & Learning Analytics
Real Time Data Aggregation
Slotta et al 2013, Orchestrating of complex inquiry: Three roles for
learning analytics in a smart classroom infrastructure
23. Social Network Analysis
Fergusen et al 2013, Visualizing Social Learning Ties by Type and Topic: Rationale and
Concept Demonstrator
24. Nanogenetic Learning Analytics
martin 2013, Nanogenetic Learning
Analytics: Illuminating Student Learning
Pathways in an Online Fraction Game
http://games.soe.ucsc.edu/proj
ect/refraction-level-generation
26. Affective States and State Tests
Intelligent Tutoring Systems
Pardoes et al 2013, Affective states and state tests: Investigating how affect
throughout the school year predicts end of year learning outcomes
29. References
• Schneider et al 2013, Toward Collaboration Sensing: Applying Network Analysis Techniques to Collaborative
Eye-tracking Data
• Worsley Bilkstein 2013, Towards the Development of Multimodal Action Based Assessment
• D’Aquin Jay 2013, Interpreting Data Mining Results with Linked Data for Learning Analytics: Motivation, Case
Study and Directions
• martin 2013, Nanogenetic Learning Analytics: Illuminating Student Learning Pathways in an Online Fraction
Game
• Fergusen et al 2013, Visualizing Social Learning Ties by Type and Topic: Rationale and Concept Demonstrator
• Pardoes et al 2013, Affective states and state tests: Investigating how affect throughout the school year
predicts end of year learning outcomes
• Slotta et al 2013, Orchestrating of complex inquiry: Three roles for learning analytics in a smart classroom
infrastructure
• Harrer 2012, Analytics of collaborative planning in Metafora - architecture, data, and analytic methods
• Abelardo 2013,. Bridging the Middle Space with Learning Analytics. Keynote presentation at the
International Conference on Learning Analytics and Knowledge, Leuven, Belgium, 10-12 April 2013.
• Lovett 2013, http://www.itif.org/files/2012-thille.pdf
• http://www.crlt.umich.edu/sites/default/files/resource_files/SLAM%2011-9-
12%20Lovett%20Presentation.pdf
• http://myles.jiscinvolve.org/wp/2013/04/10/day-1-am-learning-analytics-and-knowledge-conference-
april-2013/
• http://games.soe.ucsc.edu/project/refraction-level-generation
• www.bertrandschneider.com
Editor's Notes
Have a range of people here, so we will be addressing both the intro and the expert
Number of people being sampledNumber of variables: High dimensional data, can collect lots of different Look for new patterns within that data to create new variables New formats, not just MCQs
1. Reinventing the wheel: genius or the power of data2. No, you’re both wrong!3. Actually you could do this?4. Or this?5. Here’s an overviewThere’s two things:Power of dataBetter with more voices but those voices haven’t developed a common languageIs this something completely different?Data is model/domain agnostic
Possibilities: what kinds of opportunities does LA pursue?Buzzwords: what kinds of buzzwords are popular in the LA space?Who: who is involved are where do they come from?
Scientific discovery: discover new things about “learning” & “knowing”PersonalizationIncreased access: democratization of learning and access to dataAutomationIncreased accountabilityIncreased student achievementNon-intrusive data collection, measure by doingProfit
Marriage between:Educational data mining (EDM): computational process of discovering patterns in large data sets, involves artificial intelligence, machine learning, statistics, and database systemsAcademic analytics: use of business intelligence used in an academic settingLearning sciences: psychology applied to education, but more broadly the scientific method applied to educational problemsAnglosphere: US, Britain, Canada, AustraliaStanford, Carnegie Mellon, Columbia, Open University, University of Michigan
Sydney
1.supporting dynamic collective visualizations2. real time orchestrational logic3.ambient displays