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Impact of Faculty Learning Styles on the Integration of Media-Rich Content
into Instruction
Celeste M. Schwartz, Ph.D.Montgomery County Community College
Blue Bell, [email protected]
Background Higher Education Challenges1. 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.
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.
TheoriesLearning Styles & Technology Implementation
Based on human learning and development theories, and information systems theories
Theories UsedTechnology 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 Questions1. 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?
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 InstrumentAgeDisciplineGenderProfessional Development Integration of Media-rich content
Kolb’s Learning Style InventoryKolb’s Experiential Learning TheoryTwo 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 InventoryCombining one perception preference and one processing preference results in one of four learning styles.
1. Diverger (CE & RO)2. Converger (AC & AE) 3. Accommodator (CE & AE)4. Assimilator (AC & RO)
Learning ModesConcrete Experience (CE)
Active ReflectiveExperimentation (AE) 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 Findings1. 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.
FindingsRespondents 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 types34 divergers 50 assimilators 35 accommodators30 convergers
FindingsAnalyses 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 typePearson product moment found no significant correlation
between age and PU scoresPearson product moment found a small relationship between age
and PEOU scores.As expected there was no relationship between age and LSI type.
FindingsMain 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).
FindingsPost 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 Upper Bound Bound
Diverging 34 28.47 7.300 1.197 25.58 31.36
Assimilating 50 29.26 7.323 .98 26.88 31.64
Accommodating 35 32.34 6.949 1.180 29.49 35.19
Converging 30 33.03 5.986 1.275 29.96 36.11
Confidence Interval = 98.3%
Interpretation of the FindingsAge was not a variable that affected the PU or
PEOU scores and ultimate use of technologyLSI 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
Active Experimentation
(AE)
Watching
Reflective Observation
(RO)
Feeling
Concrete Experience (CE)Accommodating (CE/AE) Diverging (CE/RO)
Thinking
Abstract Conceptualization
(AC)
Converging (AC/AE) Assimilating (AC/RO)
.
Findings
Faculty who preferred Active Experimentation showed higher Perceived Ease of Use scores than faculty with the Reflective Observation orientation.
Implications for the FindingsProfessional Development staff should ensure
that brainstorming, discussion groups, observation and creative problem solving are integrated into the hands-on 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 StudyFull-time teaching faculty from two large
suburban mid-Atlantic community colleges.Media-rich contentOther theoriesSelf-report of attending specific professional
development
Recommendations for actionsFaculty 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.
