(Big Data) Analytics & Education Dr Brian Mac Namee Centre for Applied Data Analytics Research Applied Intelligence Research Centre Dublin Institute of Technology
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
Let s Talk About Data DB Data Warehouse DB DB DB DB DB Structured Data Data File Data File
A Student Joins Us For A Two Year Course What Digital Footprint Do We Have For Them? Year 1 Year 2 Time
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
This Is Probably The Worst Case Student Digital Footprint Year 1 Year 2 Time
Some Cases Are A Little Better Assignment Submissions Assignment Submissions Year 1 Year 2 Time
Some Cases Are Quite A Bit Better LMS Activity LMS Activity LMS Activity LMS Activity Clickers Clicker s Clickers Year 1 Year 2 Time
Some Activities Are Still Very Siloed Emails To Staff Personal Circumstances Form Emails To Staff Medical Cert Year 1 Year 2 Time
This Is Probably The Best Case Student Digital Footprint Year 1 Year 2 Time
Of Course There Are Lots Of Students! Year 1 Year 2 Time
Let s Not Forget About Data Integration Application Details Exam Boards LMS Clickers Assignments
Let s Not Forget About Data Integration Operational Databases Analytics Base Table LMS Data Warehouses & Data Marts Flat Files Other Systems
As A Contrast, It Is Worth Thinking About MOOCs Year 1 Year 2 Time
As A Contrast, It Is Worth Thinking About MOOCs Year 1 Year 2 Time
As A Contrast, It Is Worth Thinking About MOOCs Year 1 Year 2 Time
Analytics Is All About Decision Making Predictive Analytics Predictiv e Analytics Predictive Analytics Visualisation & Reporting Analytics Driven Decision Making Analytics
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.
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.
Standard Reporting: Dashboards For Teachers * www.alwaysprepped.com
Standard Reporting: Dashboards For Teachers * www.alwaysprepped.com
Standard Reporting: Dashboards For Teachers Time Saving Learning Planning * www.alwaysprepped.com
Standard Reporting: Dashboards For Students * www.khanacademy.com
Standard Reporting: Dashboards For Students Formative Assessment Gamification The Quantified Self * www.khanacademy.com
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.
Alerts: Social Networks Analysis * www.snappvis.org
Alerts: Social Networks Analysis * www.snappvis.org
Alerts: Social Networks Analysis Helps to identify students that need more attention * www.snappvis.org
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.
Predictive Modelling: Disengagement Risk * www.itap.purdue.edu/studio//signals/ * www-03.ibm.com/software/products/us/en/analytic-answers-student-retention/
Historical Training Set ID Assign 1 Attendance Forum Engagement Final Grade 001 78% 85% Medium 92% 045 54% 12% Low 32% 056 54% 89% High 67% 076 23% 99% Low 23%
Historical Training Set Learning Algorithm
Learning Algorithm Historical Training Set Prediction Model (Classifier)
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)
New Data Prediction Model (Classifier)
New Data ID No Risk 12 High 19 Medium 21 Low 61 Low Prediction Model (Classifier)
New Data Helps to identify students that need ID more Risk attention No 12 High 19 Medium 21 Low 61 Low Prediction Model (Classifier)
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.
Optimization: Optimising A Learner s Path
Training Set ID Items 100 Video 1, Assess 32, Article 7 200 Video 76, Assess 34 300 Video 1, Assess 32 400 Video 76, Assess 34 500 Video 1, Assess 32, Article 7 600 Video 76, Assess 32, Assess 34 700 Video 1, Assess 32, Article 7 800 Assess 32, Article 7 900 Video 76, Assess 34
Training Set Association Analysis Algorithm
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
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
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.
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
1
1 Lingle Analysis Engine 2
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)
1 3 Tailored Online Search Lingle Analysis Engine 2
1 3 Tailored Online Search Lingle Analysis Engine 4 Linguistic Analysis & Support 2
1 3 Tailored Online Search 2 Lingle Analysis Engine 4 Linguistic Analysis & Support 5 Content Generation (e.g. exercises, glossaries)
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. * www.dataprotection.ie
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?
Thank You