Adaptive Learning Systems

Adaptive Learning Systems

The State of the Field

What is Adaptive Learning?

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Individualized learning:

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Content difficulty

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Content form (text, video)

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Sequence of content

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Pace

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Adapted based on:

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Student knowledge, performance

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Student learning style, preferences, goals

Overview

Components Implications

Brief History

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1920’s - Personalized learning at scale (Dewey,

Montessori)

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1960’s - Mastery-based learning (Bloom)

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1970’s / 1980’s - Computer-assisted Instruction,

Cognitive Tutors

Koedinger et al., 1997; Mödritscher et al., 2004

Overview

Components Implications

Why now?

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Proliferation of learning technologies

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Students, teachers, institution

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Learning mediated through technology

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Flipped classes, MOOC’s

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Learning apps, student collaboration on forums

Overview

Components Implications

Why now?

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Volume of learning data generated

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Variety, velocity, granularity

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Improvements in “big” data analytics

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Machine learning

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Hadoop, MapReduce

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Student clustering

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Learning recommendations

Potential

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Improved student engagement

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Improved student performance on learning

outcomes

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Improved student retention in courses

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Improved research-based interventions

Magnisalis, 2011; Natriello, 2011; Poelhuber, 2008

Overview

Components Implications

Challenges

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Material:

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Bandwidth

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Laptops, tablets

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Cost to license software

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Infrastructural:

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System setup and support

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Integration with existing LMS’s

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Training and adoption curve

Overview

Components Implications

Challenges

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Pedagogical:

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Integration into existing teaching processes

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Course authoring

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Accuracy of recommendations

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Social dynamics of self-paced learning

Overview

Components Implications

Current Adaptive Landscape

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We will look at 16 major adaptive learning

providers

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These 16 are currently used in several public and

private universities, by a variety of public K-12

school districts, and by corporations and

individuals

Overview

Components Implications

Adaptive Providers

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Major companies

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Others

○ Open Learning Initiative

○ LoudCloud

○ ALEKS

Overview

Components Implications

Adaptive Providers

Overview

Components Implications

Sites of Adaptive Learning

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Arizona State University

(Knewton)

○ Emporium math courses have seen an 18 percent increase in pass rates and 47 percent drop in student withdrawals, from 2011-2013.

○ ASU leadership estimates that the institution has retained $12m in what would have been lost tuition revenue, for 2012-2013.

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University of New South Wales

(SmartSparrow)

○ Developed with 6 other Australian universities to teach threshold concepts in mechanics courses

○ Saw a decline in average course drop-out rate from 31 percent to 14 percent, even as course enrollments increased by nearly 30 percent, in 2012-2013.

Overview

Components Implications

Other Institutions

Carnegie Mellon University

New York University

University of New Hampshire

Southern New Hampshire University

American Public University

Western Governors University

Overview

Components Implications

Adaptive System Components

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Domain Model

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Learner Model

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Adaptation Model

Adaptive System Components

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Domain Model

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Learner Model

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Adaptation Model

Domain Model - Overview

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A conceptual map of the course or domain

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Course elements are assigned to nodes

○ Could be videos, articles, assignments, quizzes

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Edges are hierarchical relationships

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Linear or nonlinear

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With prerequisites defined

Nesbit, 2006; Chen, 2008; Graf & Ives, 2010

Overview

Components

Domain Model - Method

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Created and arranged either by the provider or by

teachers

○ Some adaptive systems use pre-authored content

○ Some allow for teacher authoring and arrangement

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Multiple conventions for domain representation

and navigation

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Karampiperis & Sampson, 2005; Magnisalis, 2011; Chung & Kim, 2012

Overview

Components

Domain Model - Risks

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Trade off of teacher autonomy and

effectiveness of system

○ Issues of consistency and sufficiency of metadata

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Ability for students to view their own “position”

in the domain

○ Risks of cognitive overload and frustration

Bargel et al., 2012; DiBitonto et al., 2013

Overview

Components

Domain Model - Systems

Overview

Components

Adaptive System Components

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Domain Model

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Learner Model

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Adaptation Model

Learner Model - Overview

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Model of each learner’s current knowledge state

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Either an overlay model or a stereotype model

○ Overlay - compared to the overall domain model

○ Stereotype - clustered with similar learner models ●

Stereotype model is based on assumptions

about student similarity

Overview

Components

Implications

Learner Model - Method

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Data is collected either:

○ Statically or dynamically

○ Explicitly or implicitly

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Static data

○ Cognitive characteristics, background knowledge

○ Non-cognitive - topic preference, learning goals

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Explicit data collection methods

○ Collected via pre-test, survey, feedback prompts

Overview

Components

Implications

Learner Model - Method

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Dynamic data

○ Knowledge state

○ Learning style (may be static or inferred dynamically)

○ Time spent on course element or LMS

○ Clickstream data ○ Assessment scores

○ Student feedback (prompted explicitly)

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Implicit data collection methods

○ Automatically collected through interactions with the system.

Overview

Components

Implications

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Risks for explicit data collection

○ Non-completion or inaccuracy

○ Interrupts learning process

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Risks for implicit data collection

○ Data provenance

○ Accuracy of inferences made with data ○ Data privacy issues

Overview

Components

Implications

Learner Model - Risks

Learner Model - Systems

Overview

Components

Adaptive System Components

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Domain Model

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Learner Model

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Adaptation Model

Adaptation Model - Overview

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Unit of Adaptivity - Course element being

adapted:

○ Difficulty

○ Content media

○ Sequence (micro or macro)

○ Pace

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Method of Adaptation

○ Variety of algorithm types used for different purposes

○ Direct or indirect presentation of adaptation

Overview

Components

Implications

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● Hidden Markov models - predicting likelihood of learner success

● Genetic algorithms

○ Refine models and construct optimal learning paths

from pre-tests

● Neural networks

○ Pattern recognition updated with input (eg: inferring learning styles from interactions)

Overview

Components

Implications

Adaptation Model - Method

Adaptation Model - Method

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Direct Adaptation

○ Students are given a visible recommendation for the next course element

○ Micro-level - problem feedback, explanations, links

○ Macro-level - learning path presented as optional

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Indirect Adaptation

○ Link hiding

○ Learning path presented as only option

Overview

Components

Implications

Adaptation Model - Risks

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Risks to student agency if the system controls too

much

○ Feelings of paternalism

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Systems with learner choice

○ Lack of student ability to choose optimal path

● Design of adaptation engine influences the learning

process

Overview

Components

Implications

Adaptation Model - Systems

Overview

Components

Implications - Technical

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Must be embedded in a platform

○ Integrated with LMS

○ Used in a digital textbook

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Need adequate bandwidth, available hardware,

technical support for set-up and maintenance

Overview Components

Implications - Pedagogical

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Choose an adaptive provider that fits your goals

○ Consider pedagogical goals and mode of instruction

○ How much teacher agency involved in authoring the course?

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Need faculty and student buy-in

○ Overcoming cultural resistance to new models of learning

● Need departmental or institutional buy-in

○ Freedom to experiment

○ Competency-based credit

Overview Components

Implications - Pedagogical

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Mode of instruction

○ Online course

○ Blended, face-to-face

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Teacher goals

○ Address differences in background knowledge

○ Appeal to different learning styles, goals

Overview Components

Implications - Pedagogical

● How does whole-class instruction happen when

students are not working on the same content at the same time?

○ Challenges for peer learning, mentoring, collaborative

groups, project-based learning

○ Could be an opportunity for changing how we view those

experiences

Overview Components

Implications - Research

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Opportunities

○ Improving student learning outcomes, retention,

engagement

○ Informing curricular development

○ Connections between cognitive factors and performance

(ie: self-regulation, motivation)

Overview Components

Implications - Research

● Challenges

○ Data acquisition - greater volume, variety, and

velocity of data than many teachers or researchers may be equipped to deal with

○ Data privacy - involvement of 3rd party adaptive

providers raises questions about privacy and security of data

Overview Components

Thank you!

Questions?

Michael Madaio

[email protected] @mmadaio