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Adaptive Learning for Educational Game Design Dr. Ed Lavieri

Adaptive Learning for Educational Game Design

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Educational computer games continue to overwhelm educators with design and development complexities, time, and cost and underwhelm learners regarding immersive, intuitive, and enjoyable game play. No complete model or comprehensive guideline for content development or game design exists for educators and game designers to follow in the creation of educational games that adapt to learners in real-time. This research developed and validated the ALGAE (Adaptive Learning GAme dEsign) model, a comprehensive adaptive learning model based on game design theories and practices, instructional strategies, and adaptive models. This dissertation extends previous research in game design, instructional strategies, and adaptive learning, combining these three components into a single complex model. The results of this study include the validation and applicability of the ALGAE model, benefits and challenges of using the model, and insights regarding the focused and unfocused implementation approaches. The study also reveals the cross-industry applicability of the model to include government agencies, military units, game industry, and academia.

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Page 1: Adaptive Learning for Educational Game Design

Adaptive Learning for Educational Game Design

Dr. Ed Lavieri

Page 2: Adaptive Learning for Educational Game Design

Three Roads

Page 3: Adaptive Learning for Educational Game Design

Game DesignGam

e Desig

n

Learners excited about games; not school

Excite learners about game’s content

‘to learn’ is the fundamental motivating factor

Traditional Game Design

Page 4: Adaptive Learning for Educational Game Design

Instructional Strategies

Instructional Strategies

Learner Concept Map Learning Object Ontology

Page 5: Adaptive Learning for Educational Game Design

Learning Theories

Human Behavior

Cognitive Factors

Environmental Influences

Behavior

Respondent(prior cues)(emotional)

Operant(produces

rewards/punishments)(environment)

Performs Operations(encoding)

Information

StorageRetention/Retrieval

GenerateResponse to Info

Evaluation

Synthesis

Analysis

Application

Understanding

Knowledge

Instructional Problem

Learner’s Characteristic

s

Test Analysis

Instructional Objectives

Content Sequencing

Instructional Strategies

Designing the Message

Instructional Delivery

Evaluation Instruments

P

EB

Page 6: Adaptive Learning for Educational Game Design

Adaptive LearningAdaptive Learning

Page 7: Adaptive Learning for Educational Game Design

MethodologyQualitative Exploratory

Model Based Reasoning

Tacit Epistemology

Literature

Review

Integrated

ModelSurvey Analysis Revised

Model

Page 8: Adaptive Learning for Educational Game Design

ALGAE

Adaptive Learning GAme dEsign (ALGAE)

Page 9: Adaptive Learning for Educational Game Design

ALGAE Model

Consolidated

Instructional Strategies

Model (CISM)

Page 10: Adaptive Learning for Educational Game Design

ALGAE Model

Comprehensive

Adaptive Learning

Model (CALM)

Page 11: Adaptive Learning for Educational Game Design

Survey Instrument – Area of Focus

Focus Area

0 5 10 15 20 25 30 35 40 45 50

Game Design

INS-STR

Adaptive Learning

Not Identified

Participant Primary Area of Focus

Page 12: Adaptive Learning for Educational Game Design

Survey Instrument – Expertise Levels

Game Design

INS-STR

Adaptive

Other

1 3 5 7 9 11 13 15 17 19Game Design INS-STR Adaptive Other

Very Little 1 0 0 6

Some 2 9 2 1

Above Avg 7 15 8 4

Expert 6 19 1 6

Very Little

Very Little

Very Little

Very Little

Some

Some

Some

Some

Above Avg

Above Avg

Above Avg

Above Avg

Expert

Expert

Expert

Expert

Participant Level of Expertise by Area of Focus

Page 13: Adaptive Learning for Educational Game Design

Survey Instrument - Industries

0 10 20 30 40 50 60

Government/Military

Academic

Game

Not-For-Profit

Independent

Other

Participant Industries (Select All That Apply)

• No “Independent” Respondents• No “Not-For-Profit” Respondents• “Other” Write-ins:

• Corporate Training & Development• Synthetic Intelligence• Libraries• Corporate and Non-Profit Online Training

Page 14: Adaptive Learning for Educational Game Design

Survey Instrument – Education Levels

Game Design

INS-STR

Adaptive

Oher

0 5 10 15 20 25

HS or Below

HS or Below

HS or Below

HS or Below

AS/AA

AS/AA

AS/AA

AS/AA

BS/BA

BS/BA

BS/BA

BS/BA

MS/MA

MS/MA

MS/MA

MS/MA

Doctorate

Doctorate

Doctorate

Doctorate

Participant Education Levels

Page 15: Adaptive Learning for Educational Game Design

RQ1How can an adaptive learning model’s effectiveness be assessed?

asse

ssm

ents

auto

mat

ed a

naly

tics

base

line

/ ben

chm

arks

case

stu

dies

expe

rt op

nion

lear

ner r

eten

tion

lear

ning

out

com

es

mea

sure

d ov

er ti

me

0

1

2

3

4

5

6

Page 16: Adaptive Learning for Educational Game Design

RQ2 What characteristics of an adaptive learning model need to be evaluated in order to determine the model’s efficacy?

Adapt

iven

ess

Conte

nt D

eliv

ery

Reten

tion

Lear

ning

Out

com

es

Corre

latio

n to

LOs

Usabi

lity

Enga

gem

ent

Asses

smen

t

Rewar

d Sy

stem

Attitu

de

Motiv

atio

n

Cost E

ffectiv

enes

s

Stud

ent P

erfo

rman

ce

Rapid

Feed

back

0

2

4

6

8

10

12

Page 17: Adaptive Learning for Educational Game Design

RQ3 What are the key conceptual elements of game design methodologies that should be incorporated into a comprehensive adaptive learning model?Missing

Intuitive

Transreality Component

Storage

Measurement Systems

Video Tutorials

Aesthetics

Role of Trainer

Personalization, Engaging, Challenging, Completion Certificate

Complex Open-Ended Problem Space

Unnecessary

Social Connectivity / Collaborative

Punishment System

Instructional Strategies System

Contextual Feedback

NPCs

Motivated

Survey Participant Opinion

Page 18: Adaptive Learning for Educational Game Design

RQ4 What are the key instructional strategies that should be incorporated into a comprehensive adaptive learning model?

Page 19: Adaptive Learning for Educational Game Design

RQ5What level of adaptiveness is necessary to increase learning outcomes in educational games?

 

Level of Adaptiveness

Design & DevelopmentDifficulty

 Level of AdaptivenessDesign & Development

Difficulty

Page 20: Adaptive Learning for Educational Game Design

RQ6-A What are the advantages. . .of using the ALGAE model?

I

nst.

Desig

ners

G

am

e D

esig

ners

- Structure / guidelines- Game success- Improved outcomes- Non-exclusive gameplay- Link between GD & EDU

- Structure / guidelines- Checklist / job aid- Understanding in-

game adaptivity

Page 21: Adaptive Learning for Educational Game Design

RQ6-B

What are the. . . disadvantages of using the ALGAE model?

I

nst.

Desig

ners

G

am

e D

esig

ners

- Specificity of LOs

- Difficult to comprehend- Difficult to implement- Change / buy-in

- Collaboration with educators

Page 22: Adaptive Learning for Educational Game Design

RQ7How does the ALGAE model support adaptive learning?

Content-RelatedAdapts presentation of content

Participation-based content creation

Makes content effective

Delivers adaptive content

Based on learner needs

Links Content to storyline

Instruction and goals

Goals to KSAAMs

Learner-FocusedAdaptable learning path

In-game progression drives difficultly level

Page 23: Adaptive Learning for Educational Game Design

RQ8What are the benefits and challenges when using a focused vs. an unfocused approach to implementing a specific game design model?

UnfocusedFocused

ConsistencyAccuracy

CoordinationScope Management

FlexibilityAdaptabilityCreativity

InflexibilityStifles Creativity

People

Time ConsumingHard to Implement

Implementation Approach

Page 24: Adaptive Learning for Educational Game Design

ImplicationsBasic Guideline

Checklist

Data Schema

Unknown Level of Adaptiveness (RQ5)

Figure 5.41

Opinions of Usability

Page 25: Adaptive Learning for Educational Game Design

Art of the Possible

Page 26: Adaptive Learning for Educational Game Design

Future Research

Near Term

Detailed model analysis

Focused / unfocused implementation

Assessment tool

Determine adaptiveness level

Map to serious game heuristics

Longer Term

Domain ontology & repository integration method

Learner retention methods

Deep immersion

Determine ethical, social, and cultural issues

Measuring connectivity between ALGAE components

Collaboration Invitation: MIT Education Arcade and Learning Games Network

Page 27: Adaptive Learning for Educational Game Design

Three Roads & ALGAE

Page 28: Adaptive Learning for Educational Game Design

Adaptive Learning

Instructional

Strategies

Game Design

Adaptive Learning for Educational Game Design

Dr. Ed Lavieri