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ERIC Number: EJ1113125
Record Type: Journal
Publication Date: 2016
Pages: 13
Abstractor: As Provided
ISSN: ISSN-1547-9714
Predicting the Probability for Faculty Adopting an Audience Response System in Higher Education
Chan, Tan Fung Ivan; Borja, Marianne; Welch, Brett; Batiuk, Mary Ellen
Journal of Information Technology Education: Research, v15 n395-407 2016
Instructional technologies can be effective tools to foster student engagement, but university faculty may be reluctant to integrate innovative and evidence-based modern learning technologies into instruction. Based on Rogers' diffusion of innovation theory, this quantitative, nonexperimental, one-shot cross-sectional survey determined what attributes of innovation (relative advantage, compatibility, complexity, trialability, and observability) predict the probability of faculty adopting the audience response system (ARS) into instruction. The sample of the study consisted of 201 faculty at a university in the southeastern United States. Binary logistic regression analysis was used to determine the attributes of innovation that predict the probability of faculty adopting the ARS into instruction. Out of the five attributes, compatibility and trialability made significant contributions to the model. The implication of the findings is that, in order to maximize adoption, the faculty needs to be given the opportunity to pre-test the ARS prior to implementation, and they need to know how the technology will assist them in achieving their pedagogical goals. Recommendations were made to leverage these attributes to foster faculty adoption of the ARS into instruction.
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A