Celeste M. Schwartz, Ph.D.
Montgomery County Community College
Blue Bell, Pennsylvania
cschwartz@mc3.edu
Background
Higher Education Challenges
National Education Technology Plan’s (NETP)
request that faculty use technology to create
engaging learning environments.
2. EDUCAUSE 2009 Teaching and Learning
Technology Challenges of student engagement and
faculty integration of new technologies into teaching
and learning.
3. Federal Higher Education Challenge to increase the
percentage of 2- and 4- year degree completers.
1.
Background
Why do some faculty embrace and
integrate new proven technologies
sooner than others?
The Gap
Little is known regarding different
learning styles of faculty and its
impact on their use of technology in
teaching.
Theories
Learning Styles & Technology Implementation
 Based on human learning and development theories,
and information systems theories
 Theories Used
 Technology Acceptance Model (Davis) - the perceived
usefulness and perceived ease of use of a technology
 Kolb’s Experiential Learning Theory (Kolb) - an
individual’s preferred learning style
Research Questions
Are there differences, based on their learning styles, in
community college full-time faculty’s perceived
usefulness of integrating media-rich content into their
courses, after controlling for effects due to age?
2. Are there differences, based on their learning styles, in
community college full-time faculty’s perceived ease of
integrating media-rich content into their courses, after
controlling for effects due to age?
3. Is there a significant correlation between community
college full-time faculty’s perceived usefulness of
integrating media-rich content into their courses and
their perceived ease of integrating media-rich content
into their courses?
1.
Media-rich Content Definition
Media-rich content is defined as technologies that
enable learners to participate in an engaging interactive
learning environment supported by technologies.
Media-rich content provides learners with the ability to
see, hear, and interact with multiple communication
streams synchronously and asynchronously.
Instruments used in the study
 Demographic questionnaire
 Kolb’s Learning Style Inventory (LSI)
 Davis’s Technology Acceptance Model (TAM)
Demographics Instrument
 Age
 Discipline
 Gender
 Professional Development
 Integration of Media-rich content
Kolb’s Learning Style Inventory
Kolb’s Experiential Learning Theory
 Two preference dimensions
 perception dimension - two opposite dimensions for
perception of the experience are concrete experience
(CE) and abstract conceptualization (AC)
 processing dimension - two opposite dimensions for
processing the experience are reflective observation
(RO) and active experimentation (AE).
Kolb’s Learning Style Inventory
Combining one perception preference and one processing
preference results in one of four learning styles.
1.
2.
3.
4.
Diverger (CE & RO)
Converger (AC & AE)
Accommodator (CE & AE)
Assimilator (AC & RO)
Learning Modes
Concrete Experience (CE)
Active
Experimentation (AE)
Reflective
Observation (RO)
Abstract Conceptualization (AC)
Learning Style Types
CE
Accommodator
Diverger
AE
RO
Converger
Assimilator
AC
Data Analyses
 Research Question 1 & 2 used a casual-comparative
research design
 Research Question 3 used a non-experimental
correlational design.
Davis’s Technology Acceptance
Model
Perception Survey
 Perceived Usefulness (PU)
 Perceived Ease of Use (PEOU)
Anticipated Findings
Faculty members’ preferred learning styles identified as
converging or accommodating will be more likely to
perceive usefulness of integrating media-rich content
into their courses than faculty members’ identified as
diverging or assimilating.
2. Faculty members’ preferred learning styles identified as
converging or accommodating will be more likely to
perceive ease of integrating media-rich content into their
courses than faculty members’ identified as diverging or
assimilating.
3. Significant correlation between faculty’s perceived
usefulness of integrating media-rich content into their
courses and their perceived ease of use of integrating
media-rich content into their courses.
1.
Findings
 Respondents from the sample population were 149
(valid responses) which represented a slightly higher
number of female respondents to the sample
population.
 The respondents represented 35 academic disciplines
and 5 academic divisions.
 Participants LSI types
 34 divergers
 50 assimilators
 35 accommodators
 30 convergers
Findings
 Analyses for alpha scores
 Cronbach alpha scores for LSI learning cycle mode CE, RO, AC,
&,AE were all above the acceptable value of .70.
 Cronbach alpha scores for TAM PU and PEOU were also above the
acceptable value of .70.
NOTE: Because Cronbach alphas were strong the research
questions could be examined.
 Analyses of the relationship between age and PU, age and PEOU,
and age and LSI type
 Pearson product moment found no significant correlation between
age and PU scores
 Pearson product moment found a small relationship between age
and PEOU scores.
 As expected there was no relationship between age and LSI type.
Findings
 Main Analyses
 Anova was not able to explain the observed differences
in PU scores based on LSI scores.
 Ancova was run to determine the impact of LSI type on
PEOU scores after controlling for age. The covariate age
did not appear to contribute meaning information
 Anova found that the LSI scores impacted the PEOU
scores based on the finding with a more stringent alpha
level of p <.017.
 Pearson product moment correlation found that there
was a significant positive relationship between PU and
PEOU.(p < .001).
Findings
 Post hoc analyses were performed to determine the
differences in PEOU scores by each of the LSI learning
types. As seen in the next slide accommodator and
converger have similar mean scores and the mean
scores of diverger and assimilator are similar. (NOTE:
this shows that the processing dimension is what ties
accommodator and converger together and diverger
and assimilator together
 Based on these findings PEOU data were reanalyzed
using ANOVA and showed statistically significant
results, F (1, 1470 = 10.52), p = .001
Estimated marginal means from
ANOVA of PEOU scores by LSI type
LSI Learning Type n
Mean
SD
Std. Error
Lower
Bound
Upper
Bound
Diverging
34
28.47
7.300
1.197
25.58
31.36
Assimilating
50
29.26
7.323
.98
26.88
31.64
32.34
6.949
1.180
29.49
35.19
33.03
5.986
1.275
29.96
36.11
Accommodating
Converging
35
30
Confidence Interval = 98.3%
Interpretation of the Findings
 Age was not a variable that affected the PU or PEOU
scores and ultimate use of technology
 LSI type does not help to clarify observed differences
in PU scores.
 LSI type does impact PEOU scores. It is not surprising
that active experimenters will have higher PEOU
scores compared to reflective observers. Based on the
TAM PEOU questions relating to how faculty
perceived ease of use of integrating media rich content
into their courses. the process closely aligned with the
act of doing.
Doing
Watching
Active Experimentation (AE)
Reflective Observation (RO)
Accommodating (CE/AE)
Diverging (CE/RO)
Converging (AC/AE)
Assimilating (AC/RO)
.
Feeling
Concrete Experience (CE)
Thinking
Abstract Conceptualization
(AC)
Findings
Faculty who preferred Active
Experimentation showed higher
Perceived Ease of Use scores than
faculty with the Reflective
Observation orientation.
Implications for the Findings
 Professional Development staff should ensure that
brainstorming, discussion groups, observation and
creative problem solving are integrated into the handson training.
 Professional Development staff should provide a
teaching and learning model that faculty could
emulate.
 Faculty should have an understanding of their own
learning styles and an understanding of the different
learning styles of their students.
Limitations of this Study
 Full-time teaching faculty from two large suburban
mid-Atlantic community colleges.
 Media-rich content
 Other theories
 Self-report of attending specific professional
development
Recommendations for actions
 Faculty professional development offerings should
include programs that encompass all aspects of Kolb’s
learning cycle.
 Colleges should encourage the integration of proven
technologies into teaching and learning.
 Faculty should include approaches and technologies
into their courses that take into account students’
preferred learning styles.
QUESTIONS
Celeste M. Schwartz
cschwartz@mc3.edu