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In Focus presentation: The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System
 

In Focus presentation: The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System

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The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System ...

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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  • ReportingAll sections of Intro to Bus. Session results for each objective in the learning map which synch to the course objectives
  • ReportingAll sections of Intro to Bus. Session results for each objective in the learning map which synch to the course objectives

In Focus presentation: The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System In Focus presentation: The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System Presentation Transcript

  • Delivering the Learning Ecosystem - A Content Agnostic Adaptive Learning & Analytics System
  • 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
  • The Academic Model Key Concepts Ability metrics Target knowledge Learning paths Determine knowledge Intelligent engine – adapting to learner Profiling Content www.realizeitlearning.com
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Realtime Faculty Analysis
  • Real-time Data—All Sections Report www.realizeitlearning.com
  • Introduction to Business Course— Individual results www.realizeitlearning.com
  • Real-time Data—By Instructor and Objective www.realizeitlearning.com
  • 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