The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System Presentation from 'InFocus: Learner analytics and big data', a CDE technology symposium held at Senate House on 10 December 2013. Conducted by George Mitchell (Chief Operations Officer, CCKF Ltd, Dublin). Audio of the session and more details can be found at www.cde.london.ac.uk.

1. Delivering the Learning Ecosystem
- A Content Agnostic Adaptive Learning & Analytics
System

2. Goals

Provide a personalized learning experience
o
o
o
o
o



Deliver learning at an appropriate time
Deliver appropriate learning material
Learn about the learner
Manage and adapt to change: abilities, metrics, behavior
etc.
Identify weaknesses and try to remedy
Help a learner to realize their potential
Simulate or emulate a good teacher
Remain subject and content independent
www.realizeitlearning.com

3. The Academic Model
Key Concepts
Ability metrics
Target knowledge
Learning paths
Determine
knowledge
Intelligent engine
– adapting to learner
Profiling
Content
www.realizeitlearning.com

4. Key Concepts
Target knowledge
Target
knowledge
Knowledge space

Logical connections between elements

Pre-requisite and other relationships
Domain
Element
5
Element
6
Topic
Element
3
Topic
Element
1
Area
Area
Element
1
Area
Element
2
Area
Element
3
Element
4
Area
Element
4
Element
5
Element
2
Element
7
www.realizeitlearning.com

5. Key Concepts
Target knowledge
Target
knowledge
By its very nature a competency based model

Granular elements of knowledge

Ability to track progress and attainment against
knowledge elements

Ability to track specific competencies

Ability to navigate through the elements by
demonstrating competency
www.realizeitlearning.com

6. Key Concepts
Target knowledge
Academic Independence

Maintaining academic rigor

Control of curriculum and content

Fully engaging faculty in online delivery
Real time evidence
for course evolution
Target
knowledge

7. Key Concepts
Intelligent engine
Intelligent engine
Requirements

Deliver learning suited to an individual

Adapt to responses from the individual

Evolve behavior as the system grows
Learning paths
Ability metrics
Determine
knowledge
Intelligent engine
Profiling
– adapting to learner
www.realizeitlearning.com

8. Key Concepts
Intelligent engine
Intelligent engine
Ability metrics
Measure and Predict Ability

Granular approach

Likelihood function

Gathers evidence to adjust functions

Automatically evolves and balances network
www.realizeitlearning.com

9. Key Concepts
Intelligent engine
Intelligent engine
Learning paths
Learning Paths


Paths managed dynamically
Adapt to learner experience
Element
6
Element
1
Element
5
Element
3
Element
4
Element
2
Element
7
Element
8
Element
1
Element
6
Element
3
Element
7
Element
2
Element
8
Element
5
Element
4
Element
7
Element
1
Element
2
Element
8
Element
6
Element
3
Element
4
Element
5
www.realizeitlearning.com

10. Key Concepts
Intelligent engine
Intelligent engine
Determine knowledge

Respect what the student knows

Gap analysis to identify what learner
needs to know
Knowledge Space
Knowledge required
Determine
knowledge
Determine knowledge
www.realizeitlearning.com

11. Key Concepts
Intelligent engine
Intelligent engine
Profiling
Profiling


Deliver the learning material that is most
appropriate to the learner
Different types of material vary in effectiveness for different
learners
Knowledge
element
Learner Profile
Find content
Probability of
success = 0.5
Content 1
Content 2
Content 3
Evaluate
content
Probability of
success = 0.7
Render and
delivery content
to learner
Exclude as not
suitable
www.realizeitlearning.com

12. Key Concepts
Intelligent engine
Intelligent engine
Delivering Learning Excellence

Measuring and predicting ability

Respecting what the learner already knows

Continuously adapting to the individual

Evolving its own behavior
Establishing competencies
with evidence

13. Key Concepts
Content
Content
Goals for content

Adapt to the learner

Don’t ask the same questions all the time

Vary for learner

Provide evidence for propagation network

Integrate with behavioral engine

Integrate with knowledge elements
www.realizeitlearning.com

14. Breaking Boundaries – Case Study
Truly content Agnostic
English
o Literature
o English Composition
History
o US History
Business & Accounting
o Marketing Management
o Spreadsheets
o Managing accounting
o Macroeconomics
Criminal Justice
o Introduction to American Court System
Computer Science
o Computer Networks
o Security
Science, Psychology, Engineering, Ethics
o Biology
o Systems Engineering
o Introduction to Psychology
o Student Success
Mathematics
o Introduction to Mathematics
o College Algebra
o Statistics: Data-driven Decision Making
A client’s deployment statistics for 1 year
o
o
o
o
o
50,000+ students
75,000+ course enrollments
18,000,000 unique questions generated by the Realizeit system
317,000 practices and revision interactions
60+ courses

15. Student experience

16. Student View

17. Student View—Next Steps Tab

18. Inside a Learning Node

19. Faculty experience

20. People
Section
with
Individual
Details

21. Four Key
Factors for
Faculty
Dashboard

22. Realtime
Faculty
Analysis

23. Real-time Data—All Sections Report
www.realizeitlearning.com

24. Introduction
to Business
Course—
Individual
results
www.realizeitlearning.com

25. Real-time Data—By Instructor and Objective
www.realizeitlearning.com

26. Roadmap for Transformation
A journey towards a new paradigm of teaching and learning
Competency
Based Learning
Evolved
Content
Student
Engagement
Content
Metrics
Business
Intelligence
Course
Analytics
Learning
Trends
Insights from
Data
Evolved
Curricula
Faculty
Engagement
www.realizeitlearning.com