Downloaded 19 times
Uploaded byCentre for Distance Education
In Focus presentation: The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System
The document outlines a content-agnostic adaptive learning and analytics system aiming to provide personalized learning experiences through the use of intelligent engines that adapt to individual learner profiles and metrics. Key functionalities include the ability to manage learning paths, deliver appropriate materials, and assess competencies in real-time, ensuring academic rigor while maintaining engagement for both students and faculty. A case study exemplifies its application, demonstrating significant user engagement and robust data analytics.
More Related Content
Similar to In Focus presentation: The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System
More from Centre for Distance Education
In Focus presentation: The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System
- 1. Delivering the LearningEcosystem - A Content Agnostic Adaptive Learning & Analytics System
- 2. Goals Provide a personalizedlearning 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 KeyConcepts 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 Knowledgespace 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 Byits 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 AcademicIndependence 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 Intelligentengine 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 Intelligentengine 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 Intelligentengine 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 Intelligentengine 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 Intelligentengine 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 Intelligentengine 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 forcontent 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.
- 16.
- 17.
- 18.
- 19.
- 20.
- 21.
- 22.
- 23. Real-time Data—All SectionsReport www.realizeitlearning.com
- 24.
- 25. Real-time Data—By Instructorand Objective www.realizeitlearning.com
- 26. Roadmap for Transformation Ajourney 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