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Cognitive Psychology Seminar The University of Memphis Dr. Michael M. Grant University of Memphis Dr. Jongpil Cheon Texas Tech University March 31, 2010 RECONSIDERING COGNITIVE LOAD: EXAMINING THE DIFFERENT TYPES & MEASURING THEM IF WE CAN Michael M. Grant 2010

Reconsidering Cognitive Load in Web based Instruction

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This study proposes a new instrument to measure cognitive load types related to user interface and demonstrates theoretical assumptions about different load types. In reconsidering established cognitive load theory, the inadequacies of the theory are criticized in terms of the adaption of learning efficiency score and distinction of cognitive load types. Since measurement of mental effort does not cover all types of cognitive load, a new way of isolating different loads is required. Previous studies have focused on designing interface to reduce extraneous cognitive load. However, interface may have the potential to enhance germane cognitive load because learners may construct their knowledge schemata with interface layouts.

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Page 1: Reconsidering Cognitive Load in Web based Instruction

Cognitive Psychology SeminarThe University of Memphis

Dr. Michael M. GrantUniversity of Memphis

Dr. Jongpil CheonTexas Tech University

March 31, 2010

RECONSIDERING COGNITIVE LOAD: EXAMINING THE DIFFERENT TYPES & MEASURING THEM IF WE CAN

Michael M. Grant 2010

Page 2: Reconsidering Cognitive Load in Web based Instruction

WBI

Learning Content

Pedagogical Plans

Isolation

Learning with WBI

Learner

Interface

Page 3: Reconsidering Cognitive Load in Web based Instruction

Basic assumptions – limited working memory

Extraneous cognitive load an irrelevant cognitive resource caused by the layout, navigation, structure or medium of instruction.

All are additive

Germane cognitive load

Cognitive Load Theory

an inherent cognitive resource caused by the complexity of learning content.

a relevant cognitive resource caused by learners' investment on schema construction and automation.

Intrinsic cognitive load

Page 4: Reconsidering Cognitive Load in Web based Instruction

Intrinsic cognitive load New application approaches1. Controlling the complexity of learning

contents2. Adaptive instruction with learners’

content expertise3. Fostering germane cognitive load

Extraneous cognitive load Traditional CLT research : reducing extraneous load

Schema construction & automation

Germane cognitive load

Applications of Cognitive Load Theory

Page 5: Reconsidering Cognitive Load in Web based Instruction

Cognitive Load Theory Research

Learning efficiency score = performance score & mental effort

(E = efficiency, P = performance, ME = mental effort)

(P and ME values were standardized into z scores)

Page 6: Reconsidering Cognitive Load in Web based Instruction

Intrinsic cognitive load

Extraneous cognitive load

No measurement to isolate each cognitive load type

Why lower mental effort is preferred?

Mental effort measurement“Please indicate how difficult the instruction/test you just took was by clicking on the appropriate degree of difficulty”

Germane cognitive load

Limitations

Learning efficiency concept

Negative Positive

Page 7: Reconsidering Cognitive Load in Web based Instruction

WBI

Learning Content

Pedagogical Plans

Three layers of the interaction between WBI and a learner

Learner

Interface

Connection

Page 8: Reconsidering Cognitive Load in Web based Instruction

Intrinsic cognitive load Difficulty of instruction

Extraneous cognitive load Usability

It may strengthen the theoretical foundation of cognitive load theory.

New Way to Measure Cognitive Load

Germane cognitive load Schema construction and automation

Page 9: Reconsidering Cognitive Load in Web based Instruction

Score from pretest

Measuring Intrinsic Cognitive Load

Prior knowledge

Page 10: Reconsidering Cognitive Load in Web based Instruction

(a) The menu in the instruction is easy to navigate.(b) I can identify easily where I am and where I should go. (c) The amount of information on each page was appropriate to

understand.(d) The information layout and locations are consistent throughout

the instruction.(e) Graphics or other elements on the pages are not distracting.

Measuring Extraneous Cognitive Load

Usability level

Page 11: Reconsidering Cognitive Load in Web based Instruction

(a) The interface contributed to my understanding of (learning content).

(b) The interface helped me to mentally organize the structure of (learning content).

(c) As I progressed throughout the unit, the interface helped me to relate later concepts to earlier concepts.

(d) While proceeding throughout the unit, the interface helped me to remember the structure of (learning content).

(e) When I think about what I just learned, I remember the content in terms of the interface’s layout.

Measuring Germane Cognitive Load

Engagement level (Schema construction and automation)

Pilot study with 52 undergraduate students- Reliability score = .944

Page 12: Reconsidering Cognitive Load in Web based Instruction

Participants: 40 (43 originally) undergraduates in the Journalism department

Data Collection: (a) pretest score (20 items) (b) posttest score (same 20 items) (c) engagement level of the instruction (d) usability level of the interface

Instructional Units: Introduction to Public Relations

Data Collection Procedure: Phase 1 - Pretest Phase 2 – instruction Posttest Survey on engagement & usability

Methodology

Page 13: Reconsidering Cognitive Load in Web based Instruction

Reliability test of responses to engagement items & usability items

Engagement: Cronbach’s Alpha = .914

Usability: Cronbach’s Alpha = .767All items acceptable

Results

Page 14: Reconsidering Cognitive Load in Web based Instruction

Significant difference (t = - 12.388, p < .001)

Results

Learning Performance02468

101214161820

10.18

15.03

PretestPosttest

Learning Performance

Page 15: Reconsidering Cognitive Load in Web based Instruction

Intrinsic cognitive load Pretest score invertedM = 10.18 —> M = 9.83

Extraneous cognitive load Usability score invertedM = 4.21 —> M = 1.79

Results

Germane cognitive load Engagement level M = 4.02

Page 16: Reconsidering Cognitive Load in Web based Instruction

Intrinsic cognitive load

Extraneous cognitive load

Results

Germane cognitive load

p = .294r = -.170

p < .001r = -.567

p = .539r = .100

Correlations

Page 17: Reconsidering Cognitive Load in Web based Instruction

Intrinsic cognitive load

Extraneous cognitive load

Results

Germane cognitive load

Β = .279

Learningperformance

Β = .686

Β = .024

63.4% explained

Multiple Regression

Page 18: Reconsidering Cognitive Load in Web based Instruction

Discussion

Lower extraneous tends toward higher germane

Prior knowledge was not related to extraneous or germane

Germane was the most significant predictor of performance

Indirect impact on performance by extraneous

Advancement of cognitive load theory

Practice of instructional design

Using an interface to promote germane engagement has potential

Extraneous and usability are puzzling

Page 19: Reconsidering Cognitive Load in Web based Instruction

Discussion

Limitations & Future Research

Small sample size

Self-report

Pretest score represents all prior knowledge

Specific to Web-based instruction

Difficult level could be combined to help measure intrinsic load

Moving toward more sophisticated analysis