402 how adaptive learning can help increase the adoption

Designing Learning for Engagement: Proven Ideas and New Approaches

January 28 & 29, 2015

402

How Adaptive Learning Can Help

Increase the Adoption of eLearning

Manish Gupta, G-Cube

Designing Learning for Engagement: Proven Ideas and New Approaches — January 28 & 29, 2015

Session 402 – How Adaptive Learning Can Help Increase the Adoption of eLearning – Manish Gupta, G-Cube

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Presented By: Manish Gupta, CEO G-Cube

Adaptive Learning – How it can help increase adoption of eLearning

Learn. Innovate. Share. Succeed


Current Scenario & Challenges with eLearning

What is Adaptive Learning & How it Solves these Challenges

Case Studies from Real World

Q & A

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Who are we

What do we do?

Have you heard about Adaptive learning?

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Current state of eLearning Development

Game Based Learning

Learning Nuggets

Mobile Learning

Rapid Authoring

Scenarios/Themes Highly

Interactive

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And, from all the available tech. we create this….

Welcome

Objectives

Section 1

Section 2

Section n

Assessment

Good bye!

Some brave ones are able to do this…

Welcome

Objectives

Section 1

Section 2

Section n

Assessment

Good bye!

Assessment

Lucky you!

Pass

Oops!

LMS

Assessment Score + Time Spent

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Challenges the current scenario poses…

Not personalized to my learning level

Not personalized to my learning style

Bored or confused learner Low or Forced Adoption= =

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Challenges for eLearning course designers…

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- Learners current knowledge level- Learner’s preferred learning style – visual,

auditory, or kinesthetic

You don’t have the right tools to figure out


Your thoughts?

How does a trainer overcomes these challenges in a classroom?

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Adaptive Learning - What

As per Wikipedia –

Adaptive learning is an educational method which uses computers as interactive teaching devices. Computers adapt the presentation of educational material according to students' learning needs, as indicated by their responses to questions and tasks.

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Adaptive Learning - What

Analyze learner’s current knowledge level

Analyze learner’s current affective state(Attentive? Confused? etc)

Personalize learning pathPersonalize Feedback

Personalize pedagogical strategies

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Personalized Learning Paths

Welcome

Objectives

Section 1

Section 2

Section n

Assessment

Good bye!

Welcome

Section 1 Section 2

Section 3Section 2

Section 3 Section 4

End of Program

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Real Life Example – Compliance Program

Welcome

Money Laundering & Terrorist Financing

Fraud

Money Transaction

Compliance Program

Assessment

• Each about 20 minutes long

• Taught using real life scenarios

• Job Aids at end of program

Money Laundering:Sub-topic b & c only

Fraud:All Topics + Case Study

Money Transaction:Sub-topic a & d only

Compliance Program

Assessment on short-listed topics/sub-topics

WelcomeReal-time assessment analyzes:- Score in tests- User’s offline activities- Mastery in interactivities

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Understanding Affective State of Learners

Source: Paper on Detecting Mouse Movement with … by Chen-Chung Liu and Chen-Wei Chung; Paper on Detection of Learner’s Affective State…. By GeorgiosTsoulouhas, Dimitrios Georgiou & Alexandros Karakos

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• Learner’s Attention Level• Learner’s Preferred Learning Objects

Accuracy of over 90% in predicting -


Information That Lets You Take Control

Topic 1 Topic 2 Topic 3 Topic 4

Screen 1 Screen 1 Screen 1 Screen 1




16.91%

8.09%5.86%

33.09%

6.62%

20.59%

28.68%

0.00%

10.00%

20.00%

30.00%

40.00%

MTI ST STI LTI VD MCQ EXR

Screen-wise Heat Map Learning Objects Analysis

Bored

Average Engagement

Highly Engaged

MTI Medium Text & Image VD Video

ST Short Text MCQ Assessments

STI Short Text & Image EXR Exercises

LTI Long Text & Image

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Technology Overview

Modular Course*

Learning Activities*

Continuous Assessments*

On-the-job Matrixes

Student ModelPredictive Analysis

Continuous Data for all activities/assessments

User Data

Learner’s Affective State,Learning Object Analysis

Personalized Sequencing, Pedagogy, and presentation

Course AuthorsLearners

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Case Study – Knewton & Arizona State UniversitySource: http://www.knewton.com/assets-v2/downloads/asu-case-study.pdf

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- Students with diverse backgrounds and knowledge levels- Over 30% failed to get C or higher grade in developmental Math course

Solution: Deployed Knewton Adaptive Learning System which is suitable for higher educational institutes

Problem:

http://www.knewton.com/assets-v2/downloads/asu-case-study.pdf



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Early Adopter Challenges

• Content needs to be broken down in smaller units mapped to topics/sub-topics

• Meta-tagging of all learning objects with topics/sub-topics

• No authoring tools, such as Storyline or Captivate currently support designing modular content

• Not many learning management systems in corporate learning space offering such learner modeling and predictive analysis capabilities

• No standards; can tie a client to one LMS provider

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Summary

Current e-learning is Not personalized; Leads to low adoption rates

Adaptive Learning uses learner’s current knowledge levels, data from learner activities and assessments and personalizes learning path, feedback, and presentation style

Adaptive Learning works as is seen in academia; this does require content re-configuration and LMS’s with data analytics and predictive analysis capabilities

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2 3

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Questions

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Manish [email protected]

www.gc-solutions.net

Read more about Adaptive Learning on:

Thank You


www.gc-solutions.net/blog

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mailto:[email protected]

http://www.gc-solutions.net/

http://www.gc-solutions.net/blog

402 how adaptive learning can help increase the adoption

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