CDE InFocus Conference (London): Big data in education - theory and practice


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Big Data in Education: Theory and Practice
Presented at the CDE InFocus Conference - London
December 10, 2013
Presented by Mike Moore, Sr. Advisory Consultant - Analytics
Desire2Learn, Inc.

Published in: Education
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  • How did we do that? Where did we start?First generation LMS was first step way back in 1999 – Stage OneFirst generation analytics technology added - Stage TwoSecond generation analytics technology with deeper/richer learning data curation (optimization of Stage Two tools and technology) – Stage ThreeSecond generation predictive and personalized/adaptive learning analytics with the Learner in absolute control of their destiny. Institutions are beacons for learners and drive focus and guidance with predictive and adaptive tools and technology. Institutions who employ these types of tools and technology will attract the largest student population and deliver the most skilled and capable graduates into the field. – Stage FourStage Four – I made this blue like Stage One as this will become the new normal or new baseline for learning in the 21st century. It will be the benchmark/baseline for all learning tools and technology moving forward. See next slides for more details on what is possible for learning as we move towards the end of the second decade of the 21st century. D2L is building the foundation (ie APIs, analytics/predictive analytics, adaptive, gaming, etc.) for Stage 4 entrance and expansion.Data access – just dataReporting/OLAP - what happenedForecasting – why did it happenPredictive modeling – what will happenOptimization (real-time predictive analysis)– what is the best that could happenSense and respond. Predict and Act.Marketing blurb:Already using the learning environment data available to report on key learning outcomes, student engagement and enrolment metrics as well as student grades data, Desire2Learn’s vision for big data in education was to move the institution from traditional activity reporting functions to a big data-driven framework with learning and academic analytics functionality at its core. By developing partnerships with key industry leaders in enterprise analytics applications, the Desire2Learn Analytics portfolio for education would be transformational.Understanding that big data concepts were new to education, Desire2Learn Analytics was specifically packaged into bundled offerings not only to suit different institutional reporting needs but also to address different institutional strategies around big data. Further, by developing a strategic roadmap to include predictive modules in their offering, Desire2Learn’s Analytics portfolio would offer an unprecedented suite of products with the capability to tap into the vast amounts of big data available in education today. Desire2Learn Analytics Portfolio delivers a multi-tiered analytics solution that offers customers a path forward to manage the analytics initiatives that are critical to their institutional effectiveness. Whether those analytics initiatives focus onincreasing operational efficiency, optimizing learning outcomes or creating the conditions for learner success, Desire2Learn’s Analytics Portfolio delivers two analytics solutions that are integral to your institution's strategic process improvement efforts.
  • Compatibility: Standalone componentWeb-based app (desktop or mobile)Supports IE v8.0 and above, Chrome, FirefoxIntegrates with SIS:BannerWorking on PeopleSoft integrationIntegrate with DAS:Degree Works CAPP
  • Total = % who answered correctlyUpper 27% = the percentage of ppl in the top quartile who answered the question correctlyDiscrimination index = shows discrimination between upper and lower quartile performers (generally the higher the better)If there is a negative, you prob want to remove that qu`estion from the examReliability coefficient:Only works for tests that are designed to test a coherent body of knowledge (so questions are correlated)High reliability indicates that the test is a reliable/good gauge of the student’s knowledge in a specific area (b/c noticeable patterns (correlation) will occur in student marks on the questions)B/w 0 and 1 -> >75% is very reliableHigher is goodPoint biserialCorrelation coeffecientCorrelation b/w getting the question correct and doing well on the overall examHigher is betterNegative is problematicResponse frequenciesGood at indicating effectiveness of distractor questions
  • All about aggregating outcomes across levels within the org
  • Shows relationships between learning outcomes and other learning outcomes at various organizational levelsInstitutional levelProgram levelDepartment levelCourse template level
  • Let the dataset change your mindset.We need to change our mindset when thinking about transforming our schools, and we need to be willing to "thrive on the unknown, appreciate ambiguity, and relish being different," to be willing to implement "yet-to-be-proven ideas," and to "focus on being different first and then on being better"--all of which take courage and an ability to learn as you move forward.
  • CDE InFocus Conference (London): Big data in education - theory and practice

    1. 1. You have data We provide Insights™ Big Data in Education: Theory and Practice Michael Moore, MSCIS Sr. Advisory Consultant – Analytics
    2. 2. ILP - Analytics Capability and Maturity Model Stage Four Insight and Information Value What do you want to happen for you?? Stage Three Stage One Stage Two What do I want to happen? Advanced Adaptive What will happen? What has happened? What is happening? Data Reporting Access Advanced Predictive Predictive Risk Modeling Forecasting Strategic Optimization D2L Integrated Learning and Advanced Analytics Platform
    4. 4. Optimized for Potential Performance “Our primary motivation for deploying Degree Compass was to respond to the unique success and retention needs of our complex student population.” Dr. Tristan Denley, | Austin Peay State University
    5. 5. What ingredients go into the ratings? Global centrality Major centrality Grade prediction
    6. 6. Research Data: Grade Predictions • Predicts grades to within 0.6 of a letter grade on average
    7. 7. Research Data: Grade Predictions • Typical probability of achieving A or B in a course – 62% • Degree Compass™ recommended courses – probability of A or B in a course – 85%
    8. 8. Research Data: Successful Course Completion
    9. 9. Research Data: Attainment Gap Minimized performance disparity based on • • • • Socioeconomic status Ethnicity Gender First-generation enrollments “Typical achievement gap of 20% is closed to a mere 6%.”
    10. 10. Research Data: Attainment Gap
    11. 11. Degree Compass™ Logical Diagram Student application such as Ellucian’s Banner Student Advisor application such as Ellucian’s Degree Works or CAPP Degree Audit System SIS High School transcripts, GPA, SAT s, ACTs, Class Rank, etc. Program Audit or Inventory Courses, Schedules, Core and Elective Requirements, Courses taken vs courses still needed for degree completion, etc. Degree Compass™ D2L’s Personalized, course recommendation app
    12. 12. Degree Compass™ Details • SIS Systems – Banner v8.0 • Portals – Banner Self-Service v8.0 – Luminis Portal v4.0 • Degree Audit Systems – Degree Works v4.1.0 – CAPP v8.5.3 • Web Browsers – Chrome, IE, Firefox, Safari • Mobile Platforms – iOS v4.0 (native) – Android v2.3 (non-native)
    13. 13. Science Behind Success • Degree Compass™ composed of two predictive modeling engines and a predictive algorithm – Grade Prediction Engine/Model • Provides accurate estimate of final grade student will likely receive – Centrality Prediction Engine/Model • Provides accurate ranking of a program’s courses at the institution • Degree Compass™ uses several inputs to drive predictive engines and algorithm – Student Information System (SIS) • Banner SIS – Degree Audit Systems (DAS) • DegreeWorks • CAPP
    14. 14. Degree Compass™ Roadmap • TODAY: Which {course} should I choose Incoming Freshman focus • Which {program} should I choose • Which {career} should I choose
    15. 15. Mapping this to D2L’s Solution Set • Degree Compass™ - what are your predictors for success in the program • Student Success System - at what points should we be concerned • These two together are a powerful analog for the onboarding
    16. 16. Adaptive Learning • Knowillage LeaP • Adaptive learning engine • Personalized learning experience What if textbooks could learn . . . from you?
    17. 17. Example Reports
    18. 18. Performance Analytics
    19. 19. InsightsTM Achievement Reports Module
    20. 20. Achievements by Course
    21. 21. Mapping Reports
    22. 22. Academic Risk Report
    23. 23. Learning Environment Login
    24. 24. Get to Know Your Big Data Let the dataset change your mindset. Subtitle
    25. 25. Questions to Think About… What if… • you had a reporting tool to directly support institutional improvement initiatives? • you had a reporting tool which was focused on support of student retention and improvement? • students could receive personalized content before they knew what they needed? • you could identify trends and make decisions based on free text in discussion forums?
    26. 26. Questions? Michael Moore, MSCIS Sr. Advisory Consultant - Analytics Direct 888.772.0325 x6604 Twitter: @MikeMooreD2L Thank You Subtitle
    27. 27. Let’s transform teaching and learning, together. Desire2Learn, Campus Life, CaptureCast, Desire2Learn Binder, myDesire2Learn, Insert Stuff, Insert Stuff Framework, Instructional Design Wizard, and the molecule logo are trademarks of Desire2Learn Incorporated. Subtitle The Desire2Learn family of companies includes Desire2Learn Incorporated, D2L Ltd., Desire2Learn Australia Pty Ltd, Desire2Learn UK Ltd, Desire2Learn Singapore Pte. Ltd. and D2L Brasil Soluções de Tecnologia para Educação Ltda.