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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.
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Delivering the Learning Ecosystem
- A Content Agnostic Adaptive Learning & Analytics System
www.realizeitlearning.com
Goals
Provide a personalized learning experienceo Deliver learning at an appropriate timeo Deliver appropriate learning materialo Learn about the learnero Manage and adapt to change: abilities,
metrics, behavior etc.o 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
Content
The Academic ModelKey Concepts
Target knowledge
Intelligent engine – adapting to learner
Ability metrics
Learning paths
Profiling
Determine knowledge
www.realizeitlearning.com
Key ConceptsTarget knowledge
Knowledge space Logical connections between elements Pre-requisite and other relationships
Target knowledge
Domain
Topic
Area Area
Element 1
Element 2
Area
Topic
Area Area
Element 3
Element 4
Element 5
Element 1
Element 3
Element 4
Element 2
Element 5Element
6
Element7
www.realizeitlearning.com
Key ConceptsTarget 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
Target knowledge
Key ConceptsTarget knowledge
Target knowledge
Academic Independence Maintaining academic rigor Control of curriculum and content Fully engaging faculty in online
delivery
Real time evidence for course evolution
www.realizeitlearning.com
Key ConceptsIntelligent engine
Requirements Deliver learning suited to an individual Adapt to responses from the individual Evolve behavior as the system grows
Intelligent engine
Intelligent engine – adapting to learner
Ability metrics
Learning paths
ProfilingDetermine knowledge
www.realizeitlearning.com
Key ConceptsIntelligent engine
Measure and Predict Ability Granular approach Likelihood function Gathers evidence to adjust functions Automatically evolves and balances network
Intelligent engine
Ability metrics
www.realizeitlearning.com
Key ConceptsIntelligent engine
Learning Paths Paths managed dynamically Adapt to learner experience
Intelligent engine
Learning paths
Element 1
Element 3
Element 4
Element 2
Element 5
Element6
Element7
Element8
Element 1
Element 3
Element6
Element7
Element 2
Element8
Element 5
Element 4
Element 1
Element 3
Element6
Element7
Element 2
Element8
Element 5
Element 4
www.realizeitlearning.com
Key ConceptsIntelligent engine
Determine knowledge Respect what the student knows Gap analysis to identify what learner
needs to know
Intelligent engine
Determine knowledge
Knowledge Space Determine knowledgeKnowledge required
www.realizeitlearning.com
Key ConceptsIntelligent engine
Profiling Deliver the learning material that is most
appropriate to the learner Different types of material vary in effectiveness for
different learners
Intelligent engine
Profiling
Knowledge element
Find content
Content 1
Content 2
Content 3
Probability of success = 0.5
Probability of success = 0.7
Exclude as not suitable
Evaluate content
Render and delivery content
to learner
Learner Profile
Key ConceptsIntelligent engineDelivering Learning Excellence Measuring and predicting ability Respecting what the learner already knows Continuously adapting to the individual Evolving its own behavior
Intelligent engine
Establishing competencies with evidence
www.realizeitlearning.com
Key ConceptsContentGoals 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
Content
Breaking Boundaries – Case Study
A client’s deployment statistics for 1 yearo 50,000+ studentso 75,000+ course enrollmentso 18,000,000 unique questions generated by the Realizeit systemo 317,000 practices and revision interactionso 60+ courses
Englisho Literatureo English Composition
Historyo US History
Business & Accountingo Marketing Managemento Spreadsheetso Managing accountingo Macroeconomics
Criminal Justiceo Introduction to American Court System
Computer Scienceo Computer Networks o Security
Science, Psychology, Engineering, Ethicso Biologyo Systems Engineeringo Introduction to Psychologyo Student Success
Mathematicso Introduction to Mathematics o College Algebrao Statistics: Data-driven Decision Making
Truly content Agnostic
Student experience
Student View
Student View—Next Steps Tab
Inside a Learning Node
Faculty experience
People Section with Individual Details
Four Key Factors for Faculty Dashboard
123
4
Real-time Faculty Analysis
1 2
3
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Real-time Data—All Sections Report
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Introduction to Business Course— Individual results
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Real-time Data—By Instructor and Objective
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Roadmap for Transformation
CourseAnalytics
Evolved Content
Competency Based Learning
Student Engagement
Insights from Data
Learning Trends
ContentMetrics
BusinessIntelligence
A journey towards a new paradigm of teaching and learning
Faculty Engagement
Evolved Curricula
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