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LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
LASI13-Boston Charles Lang, Garron Hillaire
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LASI13-Boston Charles Lang, Garron Hillaire

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  • 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
  • Transcript

    • 1. WIFI: Harvard Guest http://www.slideshare.net/LA-Boston
    • 2. * WIFI: “Harvard Guest” * Bathrooms * Water * Slideshare * Timing http://www.slideshare.net/LA-Boston Logistics
    • 3. 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
    • 4. What’s new here? Number of people Number of variables Number of time points Feed- back
    • 5. Who? Possibilities How? (buzzwords) Three slices
    • 6. Possibilities Personaliz -ation AccessDiscovery Auto- mation Account- ability Achieve- ment Stealth Profit
    • 7. 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
    • 8. Who? Educational Data Mining Academic Analytics Learning Sciences
    • 9. Who?
    • 10. Definition
    • 11. LASI Info http://www.solaresearch.org/events/lasi/ #lasiboston, #lasi13, #lasiuk, #LASIlocal, #LASIspain, #LASIams, #lasilyons, #lasiaalborg, @cwmlang E-Room http://bit.ly/LASI-eRoom
    • 12. Learning Analytics Summer Institute LASI-Boston LAK13 Conference Highlights Garron Hillaire
    • 13. Data Collection
    • 14. Learning Analytics Log Parsing Data Collection Harrer 2013, Analytics of collaborative planning in Metafora - architecture, data, and analytic methods
    • 15. 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.
    • 16. 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
    • 17. 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
    • 18. Data Analysis
    • 19. Learning Curve Analysis www.itif.org/files/2012-thille.pdf pslcdatashop.web.cmu.edu http://www.crlt.umich.edu/sites/default/files/resource_files/SLAM%2011-9-12%20Lovett%20Presentation.pdf
    • 20. Social Network Analysis Fergusen et al 2013, Visualizing Social Learning Ties by Type and Topic: Rationale and Concept Demonstrator
    • 21. 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
    • 22. Multi-Modal Analytics (Worsley Bilkstein 2013) Towards the Development of Multimodal Action Based Assessment
    • 23. 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
    • 24. Learning Analytics Collaboration Sensing (Schneider et al 2013) Toward Collaboration Sensing: Applying Network Analysis Techniques to Collaborative Eye-tracking Data www.bertrandschneider.com
    • 25. Questions?
    • 26. 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

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