Celeste M. Schwartz, Ph.D. Montgomery County Community College Blue Bell, Pennsylvania
[email protected]
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
