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(Big Data)
Analytics &
Education Dr Brian Mac Namee
Centre for Applied Data Analytics Research Applied Intelligence Research Centre
Dublin Institute of Technology
Data Warehouse
DB D
B DB
DB D
B DB
Data File Data
File
Analytics Driven
Decision Making
Structured Data
Visualisation & Reporting Predictive
Analytics
Predictive Analytics
Predictive Analytics
Analytics
Analytics Is All About Decision Making
Data Warehouse
DB
DB DB
DB
DB DB
Data File
Data File
Structured Data
Let’s Talk About Data
Time
Year 1 Year 2
A Student Joins Us For A Two Year Course What Digital Footprint Do We Have For Them?
Sem 1 Exam Board
Sem 2 Exam Board
Sem 1 Exam Board
Sem 2 Exam Board
Application Details
Time
Year 1 Year 2
Basic Data Collection
Time
Year 1 Year 2
This Is Probably The Worst Case Student Digital Footprint
Assignment Submissions
Assignment Submissions
Time
Year 1 Year 2
Some Cases Are A Little Better
Clickers
Time
Year 1 Year 2
LMS Activity
LMS Activity
LMS Activity
Clickers
Clickers
LMS Activity
Some Cases Are Quite A Bit Better
Personal Circumstances
Form Emails To
Staff Emails To
Staff
Time
Year 1 Year 2
Medical Cert
Some Activities Are Still Very Siloed
Time
Year 1 Year 2
This Is Probably The Best Case Student Digital Footprint
Time
Year 1 Year 2
Of Course There Are Lots Of Students!
Let’s Not Forget About Data Integration
Exam Boards
Application Details
Clickers LMS Assignments
Analytics Base Table
Operational Databases
Data Warehouses & Data Marts
Flat Files
Let’s Not Forget About Data Integration
LMS
Other Systems
As A Contrast, It Is Worth Thinking About MOOCs
Time
Year 1 Year 2
Time
Year 1 Year 2
As A Contrast, It Is Worth Thinking About MOOCs
Time
Year 1 Year 2
As A Contrast, It Is Worth Thinking About MOOCs
…
Analytics Driven
Decision Making
Visualisation & Reporting Predictive
Analytics
Predictive Analytics
Predictive
Analytics
Analytics
Analytics Is All About Decision Making
What’s the best that can happen?
What will happen next?
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?
Optimization
Predictive modelling
Forecasting/extrapolation
Statistical analysis
Alerts
Query/drill down
Ad hoc reports
Standard reports
Co
mp
etit
ive
adva
nta
ge
Degree of intelligence * Thomas H. Davenport, Jeanne G. Harris, “Competing on Analytics: The New Science of Winning, Harvard Business School
Press, 2007.
What Can Analytics Do?
What’s the best that can happen?
What will happen next?
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?
Optimization
Predictive modelling
Forecasting/extrapolation
Statistical analysis
Alerts
Query/drill down
Ad hoc reports
Standard reports
Co
mp
etit
ive
adva
nta
ge
Degree of intelligence * Thomas H. Davenport, Jeanne G. Harris, “Competing on Analytics: The New Science of Winning, Harvard Business School
Press, 2007.
What Can Analytics Do?
* www.alwaysprepped.com
Standard Reporting: Dashboards For Teachers
Time Saving
Learning
Planning
Standard Reporting: Dashboards For Students
* www.khanacademy.com
Formative Assessment
Gamification
The Quantified Self
What’s the best that can happen?
What will happen next?
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?
Optimization
Predictive modelling
Forecasting/extrapolation
Statistical analysis
Alerts
Query/drill down
Ad hoc reports
Standard reports
Co
mp
etit
ive
adva
nta
ge
Degree of intelligence * Thomas H. Davenport, Jeanne G. Harris, “Competing on Analytics: The New Science of Winning, Harvard Business School
Press, 2007.
What Can Analytics Do?
Alerts: Social Networks Analysis
* www.snappvis.org
Helps to identify students that need
more attention
What’s the best that can happen?
What will happen next?
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?
Optimization
Predictive modelling
Forecasting/extrapolation
Statistical analysis
Alerts
Query/drill down
Ad hoc reports
Standard reports
Co
mp
etit
ive
adva
nta
ge
Degree of intelligence * Thomas H. Davenport, Jeanne G. Harris, “Competing on Analytics: The New Science of Winning, Harvard Business School
Press, 2007.
What Can Analytics Do?
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
Historical Training Set
Prediction Model
(Classifier)
Learning Algorithm
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
Prediction Model
(Classifier) No ID Risk
12 High
19 Medium
21 Low
61 Low …
New Data
Prediction Model
(Classifier) No ID Risk
12 High
19 Medium
21 Low
61 Low …
Helps to identify students that need
more attention
What’s the best that can happen?
What will happen next?
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?
Optimization
Predictive modelling
Forecasting/extrapolation
Statistical analysis
Alerts
Query/drill down
Ad hoc reports
Standard reports
Co
mp
etit
ive
adva
nta
ge
Degree of intelligence * Thomas H. Davenport, Jeanne G. Harris, “Competing on Analytics: The New Science of Winning, Harvard Business School
Press, 2007.
What Can Analytics Do?
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 Rules
Video 1 Assess 32 Video 1 Article 7 Assess 32 Article 7 Video 76 Assess 32 Video 76 Assess 34
Association Analysis
Algorithm
Training Set
Association Rules
Video 1 Assess 32 Video 1 Article 7 Assess 32 Article 7 Video 76 Assess 32 Video 76 Assess 34
Association Analysis
Algorithm
Saves students time and helps them
decide the aspects of a programme to
work on
What’s the best that can happen?
What will happen next?
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?
Optimization
Predictive modelling
Forecasting/extrapolation
Statistical analysis
Alerts
Query/drill down
Ad hoc reports
Standard reports
Co
mp
etit
ive
adva
nta
ge
Degree of intelligence * Thomas H. Davenport, Jeanne G. Harris, “Competing on Analytics: The New Science of Winning, Harvard Business School
Press, 2007.
What Can Analytics Do?
Analytics Gives Rise To Possibilities For New
Data-Driven tools
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
Lingle Analysis Engine
2
Assign documents a difficulty level
Extract key grammatical features from the text (e.g. instances of tenses or phrasal verbs)
1
Lingle Analysis Engine
Tailored Online Search
3
2
1
Lingle Analysis Engine
Tailored Online Search
Linguistic Analysis &
Support
3
4
2
1
Lingle Analysis Engine
Tailored Online Search
Linguistic Analysis &
Support
Content Generation
(e.g. exercises, glossaries)
3
5
4
2
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?
Are we obliged to tell students if a
system has made a prediction about
them?
Are predictions of any use to
students?
How much of this can we turn
around onto teachers?
Thank You
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