Download - CAEL 2015 - Backward (Instructional) Design: Updating ID Toolboxes for Personalized, Adaptive, & CBE

2015 International Conference • Baltimore MD

Bishop, Ford, & Mott: Backward (Instructional) Design: Updating ID Toolboxes for Personalized, Adaptive, & CBE

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Backward (Instructional) Design Updating ID Toolboxes for Personalized, Adaptive, & CBE

CAEL 2015 INTERNATIONAL CONFERENCE, BALTIMORE MD

Jon Mott, PhDChief Learning Officer

MJ Bishop, PhDDirector, Center for Academic Innovation

Cristi Ford, PhDAssistant Vice Provost for

Learning Innovation Initiatives



@drmjbishop@drmotx@jonmott

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Agenda

Welcome & Workshop OutcomesContext: Problem, Opportunity, & DefinitionsCASE STUDIESNew Tools for the ID ToolboxReport Outs & Takeaways

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Welcome & Workshop Outcomes 4

Workshop participants will be able to:

1. Apply working definitions of personalized, adaptive, and CBE in design contexts;

2. Design the right personalized, adaptive, and / or competency-based sequences and workflows for the right contexts using the right tools;

3. Evaluate tools, platforms, and courseware based on updated toolbox (ask "hard" questions about "black boxes");

4. Design personalized, adaptive, and CBE programs and courses utilizing new tools and frameworks



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CONTEXTProblems



Curriculum & courses are (generally): 6

TIMEBOUND LINEAR

TEACHERCENTRIC

ONE-SIZE-FITS-ALL



Assessment is frequently:

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DESIGNEDFOR

SCALABILITY

NORMREFERENCED

DIGITALKINKOS

FORCOURSES

SUPPORTSTAFFFOR

SMEs

IDs are perceived as:



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CONTEXTOpportunities



9

Learning Design

Alignment

Capabilities

ActivitiesMeasurement

Policies & Assessments

Backward Design



10Criterion-referenced assessment (CBE)● Establish unambiguous,

objective measures of capability

● Hold all learners to same standard



11Authentic learning & assessment● Match learning and

assessment to real-world performance

● Match learning and assessment contexts as closely as possible



12Rethink role of instructional designers ● Professional

peers● Unique expertise

and skillsets ● Equal partners in

process



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DEFINITIONS



14Personalized learning

PreferencePlacementPathPace

4 Ps of Personalization



15Adaptive learning

PreferencePlacementPathPace

AdaptiveLearning

● Multiple remediation loops

● Pacing / path rules

● Algorithms● Predictive

analytics



16Competency-based education

FORMATIVE

DIAGNOSTIC

SUMMATIVE

CAPABILITY

TRANSCRIPT

KNOWLEDGE, SKILLS, ABILITIES

CAREER & LIFE

GOALS

PLA

LEARNIN

G

ASSESSMEN

TS

PERSONALIZED& ADAPTIVE

LEARNING PLAN

COURSEWARE

ACTIVITIES

SIMULATIONS

PROJECTS

INTERACTIONS

Remedial | O

n track | Advanced



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CASE STUDIES



18CASE STUDIESINTRO TO ACCOUNTING

● Positioned as “weed out” course

● Wide variability in incoming preparation

● Varied pace of learning

● Sporadic / intermittent demand for remediation

● Instructor burnout

GE COURSE w/ HIGH DFWs

● Required GE course● Negative student

course & faculty ratings

● High enrollments ● Varied approaches

across sections of the course

● Wide variation in preparation and performance

STUDENT TEACHER & MATH

● Student returned to school after 15 years

● Earned teaching degree with a 3.0 GPA

● Proved incapable of calculating fractions when teaching students



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NEW TOOLS for TOOLBOX



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Learning Design

Alignment

Capabilities

ActivitiesMeasurement

Policies & Assessments

Backward Design



21Authentic PLA, diagnostics, formative assessment ● What are you

measuring? ● How often will you

measure it? ● Will it be computer or

human graded? ● What is the

relationship between formative & summative assessment?



22Rules & algorithm-based personalization & adaptive

LEARNER

ACTIVITY

ACTIVITYEVALUATION

PERSONALIZATION RULE or

ALGORITHM

UPDATED LEARNING

PLAN

TYPE? NUMBER?

FREQUENCY?

LATENCY?

WHAT RULE(S)?

ALGORITHM(S)?

FREQUENCY?MANNER?



Other tools and considerations 23

PROGRAMLEVEL

PERSONALIZATION

DATA, TRACKING & ANALYTICS

CONTENTCREATION

&CURATION

EFFICACYEVALUATION



INTEROPERABILITY! 24

LTI 2.0 ANALYTICS CBE + eT



The right tool for the right context 25

Intensity / frequency of teacher-student interaction

Cognitive complexity of task

Cont

ent

/ act

ivit

y vo

lum

e re

quir

emen

tsM

achi

ne s

cora

ble

/ gui

dabl

e

AdaptiveLearning

Personalized

Learning Program-level

Personalized Learning

Knowledge • Comprehension • Application • Analysis • Synthesis • Evaluation

Adaptive Practice & Test Prep Personalize

courseware & learning sequences

Program mapping,

delivery, & analytics



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REPORT OUTSNEXT STEPS

[email protected]

© 2015 Learning Objects.

non-integratedLMSb

(or faculty website, publisher

course, etc.)

LMSa

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SIS ProgramSystem of

Record

Personalized & AdaptiveCourseware

CapabilityDashboard

Capability

Transcript

COMPREHENSIVE

PROGRAMPERFORMANCE

DATA

● LMSs● 3rd Party Courseware● Proprietary Learning Tools ● Assessment Tools

GRADES (manual)

COMPREHENSIVE PROGRAM DESIGN & DELIVERY TOOLS & SERVICES

Learning Technology

& Curriculum

Design Services

Learning-centered architecture