Adaptive Learning - CETLA · 2015. 12. 8. · Knewton Adaptive Learning Platform Knewton technology...

AdaptiveLearning

Dr. Harry KeelingAssociate Professor

Dept. of Systems and Computer ScienceCollege of Engineering, Architecture and Computer

Sciences

Reality Check

• We are all great teachers

• We choose to teach because we want to help students reach their educational goals

• Our classes are engaging, creative and we focus on making our students passionate about the subject we are teaching

• But, lets face it, when we put our content online some of our teaching skills are lost

What is Lost?

• We can’t adapt our teaching skills to the needs of our students

• We can’t:

– Judge their engagement

– Stop to explain things better

– Answer individual questions in real time

• Frankly, some of our effectiveness is lost

What do our online courses need?

• Ways to impart our teaching expertise in our online course content and delivery methods

• Ways to adjust our delivery of content to individual student needs

• Ways to provide remediation to students when they need it

• Ways to improve our online teaching

Presentation Outline

• What is adaptive learning?

• Why do we need it?

• How does it all work?

• How can we benefit?

• What does industry have to offer?

• Adaptive learning Research at HU

Definitions

• AL is a data-driven approach to instruction and remediation adjusting to students' demonstrated performance to provide content and learning resources.

• Adaptive learning blends the elements of personal instruction, peer tutoring, interactive simulations and other learning apps to create a more personalized college experience.

• AL is a more personalized, technology-enabled, data-driven approach to teaching and learning as measured by student engagement, persistence and outcomes.

• a sophisticated, data-driven, and in some cases, nonlinear approach to instruction and remediation, adjusting to a learner's interactions and demonstrated performance level, and subsequently anticipating what types of content and resources learners need at a specific point in time to make progress

Definitions

• Adaptive learning is a data-driven approach to instruction and remediation adjusting to students' demonstrated performance to provide content and learning resources.

• Adaptive learning blends the elements of personal instruction, peer tutoring, interactive simulations and other learning apps to create a more personalized college experience.

• Adaptive learning is a more personalized, technology-enabled, data-driven approach to teaching and learning as measured by student engagement, persistence and outcomes.

• Adaptive learning is a sophisticated, data-driven, and in some cases, nonlinear approach to instruction and remediation, adjusting to a learner's interactions and demonstrated performance level, and subsequently anticipating what types of content and resources learners need at a specific point in time to make progress

Definitions

• Adaptive learning is a data-driven approach to instruction and remediation adjusting to students' demonstrated performance to provide content and learning resources.

• Adaptive learning blends the elements of personal instruction, peer tutoring, interactive simulations and other learning apps to create a more personalized college experience.

• Adaptive learning is a more personalized, technology-enabled, data-driven approach to teaching and learning as measured by student engagement, persistence and outcomes.

• Adaptive learning is a sophisticated, data-driven, and in some cases, nonlinear approach to instruction and remediation, adjusting to a learner's interactions and demonstrated performance level, and subsequently anticipating what types of content and resources learners need at a specific point in time to make progress

What Does Adaptive Learning Mean?

Adaptive

Placement testPre-setCategoriesHonors/Remedial

Rule-basedDecision-tree

Learns about youData-drivenImproves

How does it work?

Expert Model vs Student Models

Expert Cognitive Model

Individual Learner Cognitive Models

More Big Data?

• Long term longitudinal data can be collected year after year as students progress through the curriculum

• A very detailed student model could help steer learners through instructional pathways that can be adjusted based on data

• Educators can analyze data from thousands of students to see if a particular piece of content is working

• Learners with the same set of characteristics could pointed to the most effective content

• Content could become “smarter” - no more one size fit all

Why We Need Adaptive Learning

• 1 out of 3 freshmen don’t come back for their sophomore year

Interest in Adaptive Learning is Heating Up

• Targeted grant program from the Bill and Melinda Gates Foundation

• New partnerships among education publishers and adaptive platform providers

• a growing list of product vendors

Benefits of Adaptive Learning

• In an August, 2013 letter to President Obama, his Council of Advisors on Science and Technology (PCAST) recommended “the production and implementation of online courses, especially when student learning is being assessed and material adjusted to the student’s needs”.

• ASU/Knewton project in 2013 when it launched its Adaptive Learning Market Acceleration Program (ALMAP), pointing to a reported 18 percent increase in pass rates and a 47 percent drop in student withdrawals

• McGraw-Hill Education’s LearnSmart is being used in a study of community college students and it is claimed that this effort has achieved a 12.5% higher course pass rate and a 10.5% increase in retention rates

• As a result of using Smart Sparrows Adaptive eLearning within courses at University of New South Wales, there was a reduction in student failure rate from 31% to 7%

Uncoupling the Iron Triangle of Higher Education

• Attempting to increase access threatens to raise costs and reduce quality

• Attempting to contain costs while maintaining quality will reduce access

• Focusing on quality while increasing access must seemingly drive up costs

3 Lenses Through Which to View Adaptive Learning Systems

• Approach describes the mechanics of “how” suppliers’ solutions employ adaptivity to create a more personalized learning environment

• Taxonomy illustrates primary product attributes vis-à-vis a set of pedagogical considerations

• Maturity addresses a core set of operational and technology attributes that any institution will need to understand relative to product adoption

Different Adaptive Learning Approaches

• Facilitator Driven – occurs when instructors receive robust, yet actionable student and cohort performance profiles (i.e. dashboards) enabling them to differentiate instructional experiences for students

• Assessment Driven – is enabled by ongoing evaluation of learner performance and/or mastery that results in fairly dynamic adjustments in he instructional content, learning resources, and course pathways

The Adaptive Learning Vendor Landscape

How ALEKS Adaptive Learning Technology Works

ALEKS online mathematics learning system from McGraw-Hill Education uses an adaptive skill assessment and artificial intelligence to continuously cater instruction to each learner's strengths, knowledge gaps, and style.

Knewton Adaptive Learning Platform

Knewton technology makes it possible for anyone to build the world’s most powerful adaptive learning applications. Knewton technology consolidates data science, statistics, psychometrics, machine learning, tagging, and unified learning histories in one place in order to enable personalization at massive scale.

Adaptive Learning at Colorado Technical University

Research Results – Spring 2012

The data revealed that there was a 12% increase in performance among those students who made use of online tutoring

Research Results – Spring 2014

The data obtained in this experiment supported the hypothesis that an adaptive framework that combines learning objectives and the content types as context dimensions can positively impact student performance.