Analytics & Education

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Lynette Ray
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1 (Big Data) Analytics & Education Dr Brian Mac Namee Centre for Applied Data Analytics Research Applied Intelligence Research Centre Dublin Institute of Technology

2 Analytics Is All About Decision Making D B Data Warehouse D B D B Data File Data File D B D B D B Structured Data Predictive Analytics Predictive Analytics Predictive Analytics Analytics Visualisation & Reporting Analytics Driven Decision Making

3 Let s Talk About Data DB Data Warehouse DB DB DB DB DB Structured Data Data File Data File

4 A Student Joins Us For A Two Year Course What Digital Footprint Do We Have For Them? Year 1 Year 2 Time

5 Basic Data Collection Application Details Sem 1 Exam Board Sem 2 Exam Board Sem 1 Exam Board Sem 2 Exam Board Year 1 Year 2 Time

6 This Is Probably The Worst Case Student Digital Footprint Year 1 Year 2 Time

7 Some Cases Are A Little Better Assignment Submissions Assignment Submissions Year 1 Year 2 Time

8 Some Cases Are Quite A Bit Better LMS Activity LMS Activity LMS Activity LMS Activity Clickers Clicker s Clickers Year 1 Year 2 Time

9 Some Activities Are Still Very Siloed s To Staff Personal Circumstances Form s To Staff Medical Cert Year 1 Year 2 Time

10 This Is Probably The Best Case Student Digital Footprint Year 1 Year 2 Time

11 Of Course There Are Lots Of Students! Year 1 Year 2 Time

12 Let s Not Forget About Data Integration Application Details Exam Boards LMS Clickers Assignments

13 Let s Not Forget About Data Integration Operational Databases Analytics Base Table LMS Data Warehouses & Data Marts Flat Files Other Systems

14 As A Contrast, It Is Worth Thinking About MOOCs Year 1 Year 2 Time

15 As A Contrast, It Is Worth Thinking About MOOCs Year 1 Year 2 Time

16 As A Contrast, It Is Worth Thinking About MOOCs Year 1 Year 2 Time

17 Analytics Is All About Decision Making Predictive Analytics Predictiv e Analytics Predictive Analytics Visualisation & Reporting Analytics Driven Decision Making Analytics

18 Competitive advantage What Can Analytics Do? Optimization Predictive modelling What s the best that can happen? What will happen next? Forecasting/extrapolation Statistical analysis Alerts Query/drill down Ad hoc reports Standard reports What if these trends continue? Why is this happening? What actions are needed? Where exactly is the problem? How many, how often, where? What happened? Degree of intelligence * Thomas H. Davenport, Jeanne G. Harris, Competing on Analytics: The New Science of Winning, Harvard Business School Press, 2007.

19 Competitive advantage What Can Analytics Do? Optimization Predictive modelling What s the best that can happen? What will happen next? Forecasting/extrapolation Statistical analysis Alerts Query/drill down Ad hoc reports Standard reports What if these trends continue? Why is this happening? What actions are needed? Where exactly is the problem? How many, how often, where? What happened? Degree of intelligence * Thomas H. Davenport, Jeanne G. Harris, Competing on Analytics: The New Science of Winning, Harvard Business School Press, 2007.

20 Standard Reporting: Dashboards For Teachers *

21 Standard Reporting: Dashboards For Teachers *

22 Standard Reporting: Dashboards For Teachers Time Saving Learning Planning *

23 Standard Reporting: Dashboards For Students *

24 Standard Reporting: Dashboards For Students Formative Assessment Gamification The Quantified Self *

25 Competitive advantage What Can Analytics Do? Optimization Predictive modelling What s the best that can happen? What will happen next? Forecasting/extrapolation Statistical analysis Alerts Query/drill down Ad hoc reports Standard reports What if these trends continue? Why is this happening? What actions are needed? Where exactly is the problem? How many, how often, where? What happened? Degree of intelligence * Thomas H. Davenport, Jeanne G. Harris, Competing on Analytics: The New Science of Winning, Harvard Business School Press, 2007.

26 Alerts: Social Networks Analysis *

27 Alerts: Social Networks Analysis *

28 Alerts: Social Networks Analysis Helps to identify students that need more attention *

29 Competitive advantage What Can Analytics Do? Optimization Predictive modelling What s the best that can happen? What will happen next? Forecasting/extrapolation Statistical analysis Alerts Query/drill down Ad hoc reports Standard reports What if these trends continue? Why is this happening? What actions are needed? Where exactly is the problem? How many, how often, where? What happened? Degree of intelligence * Thomas H. Davenport, Jeanne G. Harris, Competing on Analytics: The New Science of Winning, Harvard Business School Press, 2007.

30 Predictive Modelling: Disengagement Risk * * www-03.ibm.com/software/products/us/en/analytic-answers-student-retention/

31 Historical Training Set ID Assign 1 Attendance Forum Engagement Final Grade % 85% Medium 92% % 12% Low 32% % 89% High 67% % 99% Low 23%

32 Historical Training Set Learning Algorithm

33 Learning Algorithm Historical Training Set Prediction Model (Classifier)

34 New Data ID Assign 1 Attendance Forum Engagement 12 53% 32% Low 19 32% 65% Medium 21 21% 78% High 61 94% 10% High Prediction Model (Classifier)

35 New Data Prediction Model (Classifier)

36 New Data ID No Risk 12 High 19 Medium 21 Low 61 Low Prediction Model (Classifier)

37 New Data Helps to identify students that need ID more Risk attention No 12 High 19 Medium 21 Low 61 Low Prediction Model (Classifier)

38 Competitive advantage What Can Analytics Do? Optimization Predictive modelling What s the best that can happen? What will happen next? Forecasting/extrapolation Statistical analysis Alerts Query/drill down Ad hoc reports Standard reports What if these trends continue? Why is this happening? What actions are needed? Where exactly is the problem? How many, how often, where? What happened? Degree of intelligence * Thomas H. Davenport, Jeanne G. Harris, Competing on Analytics: The New Science of Winning, Harvard Business School Press, 2007.

39 Optimization: Optimising A Learner s Path

40 Training Set ID Items 100 Video 1, Assess 32, Article Video 76, Assess Video 1, Assess Video 76, Assess Video 1, Assess 32, Article Video 76, Assess 32, Assess Video 1, Assess 32, Article Assess 32, Article Video 76, Assess 34

41 Training Set Association Analysis Algorithm

42 Training Set Association Analysis Algorithm Association Rules Video 1 Assess 32 Video 1 Article 7 Assess 32 Article 7 Video 76 Assess 32 Video 76 Assess 34

43 Training Set Saves students time and helps them decide the aspects of a programme to work on Association Analysis Algorithm Association Rules Video 1 Assess 32 Video 1 Article 7 Assess 32 Article 7 Video 76 Assess 32 Video 76 Assess 34

44 Competitive advantage What Can Analytics Do? Optimization What s the best that can happen? Predictive modelling Forecasting/extrapolation Statistical analysis What will happen next? Analytics Gives Rise To Possibilities For New Data-Driven tools Alerts What if these trends continue? Why is this happening? What actions are needed? Query/drill down Ad hoc reports Standard reports Where exactly is the problem? How many, how often, where? What happened? Degree of intelligence * Thomas H. Davenport, Jeanne G. Harris, Competing on Analytics: The New Science of Winning, Harvard Business School Press, 2007.

45 lets teachers search for texts to use in their classes that: Are authentic texts from newspapers, magazines etc Are at the right level for students Students will be interested in Are right up to date Include the grammar teachers need to teach

46 1

47 1 Lingle Analysis Engine 2

48 1 2 Lingle Analysis Engine Assign documents a difficulty level Extract key grammatical features from the text (e.g. instances of tenses or phrasal verbs)

49 1 3 Tailored Online Search Lingle Analysis Engine 2

50 1 3 Tailored Online Search Lingle Analysis Engine 4 Linguistic Analysis & Support 2

51 1 3 Tailored Online Search 2 Lingle Analysis Engine 4 Linguistic Analysis & Support 5 Content Generation (e.g. exercises, glossaries)

52 1. Obtain and process the information fairly 2. Keep it only for one or more specified and lawful purposes 3. Process it only in ways compatible with the purposes for which it was given to you initially 4. Keep it safe and secure 5. Keep it accurate and up-to-date 6. Ensure that it is adequate, relevant and not excessive 7. Retain it no longer than is necessary for the specified purpose or purposes 8. Give a copy of his/her personal data to any individual, on request. *

53 Ethical Issues Is it okay to treat different students differently? How much of this can we turn around onto teachers? Are we obliged to tell students if a system has made a prediction about them? Are predictions of any use to students?

54 Thank You

Outline. BI and Enterprise-wide decisions BI in different Business Areas BI Strategy, Architecture, and Perspectives

Outline. BI and Enterprise-wide decisions BI in different Business Areas BI Strategy, Architecture, and Perspectives 1. Introduction Outline BI and Enterprise-wide decisions BI in different Business Areas BI Strategy, Architecture, and Perspectives 2 Case study: Netflix and House of Cards Source: Andrew Stephen 3 Case