Cognitive Styles and a Visually-oriented Component of Online Instruction

1
Cognitive Styles and a Visually-oriented
Component of Online Instruction

• Key Presenter Jia-Ling Lee
• PhD candidate
• University of Central Florida
• Co-Presenter Gary Orwig Ph.D.
• Professor
• University of Central Florida

2
Introduction

• Purpose of Study
• Research questions
• Hypotheses
• List of Terms
• Significance of Study

3
Purpose of Study

• This study will examine whether a persons
  cognitive style can be a predictor for
  visually-oriented components in online
  environments for a higher education course.

4
Research questions

• Is there a significant difference in students
  learning achievement based on their cognitive
  styles treatments
• Is there a significant difference in students
  attitudes based on their cognitive styles and
  treatments
• Can students learning achievement be predicted
  from their cognitive styles treatments
  attitudes or combination

5
List of Terms

• Cognitive Styles
• the information processing habits representing
  the learners typical mode of perceiving
  thinking problem solving and remembering
  (Chinien Boutin 1993 p. 303).
• Web-enhanced courses
• Mainly face-to-face mode
• Some instructions in a web-based courseware

6
List of Terms (cont.)

• Learning achievement
• Treatment
• Two versions of video-editing modules in WebCT
• Pretest Posttest

7
Significance of Study

• Explore cognitive styles in online learning
  environments visually-oriented tasks
• Help educators plan their online instructions
• Examine the similarities and differences between
  online and traditional environments

8
Literature Review

• Cognitive Styles
• Distance Education
• Instructional design for a visually-oriented
  component of online instruction
• Prior knowledge
• Learners attitudes and motivation

9
Research Design and Methods

• Population and sample identification
• Data Collection Procedures
• Instrumentations
• Data Analysis
• Limitations of the research

10
Population and sample identification

• Subjects
• One section of prospective teachers in the
  Introduction to Educational Technology (EME 2040)
  course
• 32 students participated in the study 28 of them
  completed all instruments.

11
Data Collection Procedures

• O1 R O2 O3 X1 O4 O5
• O1 R O2 O3 X2 O4 O5

GEFT (O1)
Field Dependent Students (FD)
Field Neutral Students (FN)
Field Independent Students (FI)
½ SD
1SD
1SD
½ SD
Mean
12
Data Collection Procedures

• O1 R O2 O3 X1 O4 O5
• O1 R O2 O3 X2 O4 O5

Field Dependent Students (FD)
Field Neutral Students (FN)
Field Independent Students (FI)
13
Data Collection Procedures

• O1 R O2 O3 X1 O4 O5
• O1 R O2 O3 X2 O4 O5

O2 O2
O3 O3
X1 X2
O4 O4
O5 O5
Treatments (X1 X2) Two versions of
video-editing modules on WebCT
Pretest 1 (O2) Attitudes Toward Computer
Technology instrument
Pretest 2 (O3) Pretest
Posttest (O4) Posttest
Questionnaire (O5)
14
Instrumentations

• Group Embedded Figures Test instrument (GEFT)
• Attitudes Toward Computer Technology instrument
  (Delcourt and Kinzie1993)
• Pretest
• Treatment A B
• Posttest
• Questionnaire

15
Treatment A B

• Reference Sources
• Textbook -- Digital Video for Dummies (Underdahl
  2003)
• Website -- Windows Movie Maker website created by
  Microsoft (Microsoft Corporation 2005)
• Instructional strategies based on Chen
  Macredies model (2002)

16
Data Analysis

• Conduct a true experimental design
• Utilize two statistic methods
• The repeated measure with two between factors
• Two-way ANOVA
• A multiple regression test

17
Limitations of the research

• Students learning behaviors may be different in
  a web-enhanced course than that in a totally
  online course
• Unbalanced gender rate
• Some instruments only have expert validity

18
Results

• Is there a significant difference in students
  learning achievement based on their cognitive
  styles treatments
• There was a statistically significant difference
  in students learning achievement. (F122143.84
  plt0.01)
• 87 of variance could be explained for by the
  achievement
• There was not a statistically significant
  difference in students learning achievement
  based on their cognitive styles (F2220.81
  pgt0.05) treatments (F1222.7 pgt0.05) or the
  interaction between their cognitive styles and
  treatments (F2221.15 pgt0.05).

19
Results (cont.)

• Is there a significant difference in students
  attitudes based on their cognitive styles and
  treatments
• There was not a statistically significant
  difference in students attitudes about computer
  anxiety toward computer technology based on their
  cognitive styles (F2223.19 pgt0.05)
  treatments (F12216.36 pgt0.05) or the
  interaction between their cognitive styles and
  treatments (F22211.48 pgt0.05).
• There was not a statistically significant
  difference in students attitudes about computer
  usefulness toward compute technology based on
  their cognitive styles (F2223.09 pgt0.05)
  treatments (F12215.34 pgt0.05) or the
  interaction between their cognitive styles and
  treatments (F22227.87 pgt0.05).

20
Results (cont.)

• Can students learning achievement be predicted
  from their cognitive styles treatments
  attitudes or combination
• No statistically significant relationship was
  found in students learning achievement among
  their cognitive styles treatments prior
  knowledge attitudes toward computer technology
  and the interaction between students cognitive
  styles and treatment groups (N28 F8191.29
  plt0.05).

21
Results (cont.)

• The equation for research question 2
• Y (Prediction of the learning achievement)
  26.514 -13.426(FD style) -11.915(FN style)
  -26.700(Treatment groups) 25.514 (Interaction
  between FD style and FI treatment) 28.862
  (Interaction between FN group and FI treatment)
  .106(pretest) -.776(Computer anxiety/comfort)
  1.891 (Computer usefulness)

22
Discussion

• Both FD and FI learners could perform equally
  well in completing visually-oriented tasks in
  online learning environments
• Small sample size
• Type of the course was different
• Some unexpected variables

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Conclusions

• Cognitive styles were not good predictors in this
  study
• FD and FI learners can perform equally well in
  completing visually-oriented tasks in online
  learning environments
• A larger sample size is needed
• Other affective factors need to be considered
• Several experiment procedures need to be
  concerned for the future studies

24
QA
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Characteristics and learning patterns of
Field-Dependent and Field-Independent
individuals (Chen Marcredie 2002. Adapted
with permission)
Hypermedia Learning Systems
Non-linear Learning
Learner Control
Multiple Tools
Prefer Guided Navigation
Prefer Maps
Prefer Free Navigation
Prefer Index
Guided Learning
Independent Learning
Passive Approach
Externally Directed
Global Fashion
Active Approach
Internally Directed
Analytic Fashion
Field Dependent Individuals
Field Independent Individuals
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An example of the guided navigation for FD
learners (Treatment A)
27
An example of the navigation icon for FD learners
(Treatment A)
28
An example of the hangman game for FD learners
(Treatment A)
29
An example of the independent learning for FI
learners (Treatment B)
Action Menu is different from Treatment A
30
An example of a step-by-step procedure for FD
learners (Treatment A)
31
An example of the search feature for FI learners
(Treatment B)
32
An example of the index feature for FI learners
(Treatment B)