Learner Modal Preference and Content Delivery Method Predicting Learner Performance and Satisfaction

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The purpose of the study was to investigate how the online, computer-based learner's personal learning profile (Preference), the content delivery method supplemented with visual content based on Neil Fleming's VARK (visual, aural, read/write, kinesthetic) model (Content), and the interaction of Preference and Content, influenced learner performance (Performance) and/or learner self-reported satisfaction (Satisfaction). Participants were drawn from a population of undergraduates enrolled in a large public southwestern research university during the fall 2015 semester. The 165 student participants (13.79% completion rate) were comprised of 52 (31.5%) females and 113 (68.5%) males age 18-58+ years with 126 (76.4%) age 18-24 years. For ... continued below

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Copeland, Matthew Blair August 2016.

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  • Copeland, Matthew Blair

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Description

The purpose of the study was to investigate how the online, computer-based learner's personal learning profile (Preference), the content delivery method supplemented with visual content based on Neil Fleming's VARK (visual, aural, read/write, kinesthetic) model (Content), and the interaction of Preference and Content, influenced learner performance (Performance) and/or learner self-reported satisfaction (Satisfaction). Participants were drawn from a population of undergraduates enrolled in a large public southwestern research university during the fall 2015 semester. The 165 student participants (13.79% completion rate) were comprised of 52 (31.5%) females and 113 (68.5%) males age 18-58+ years with 126 (76.4%) age 18-24 years. For race/ethnicity, participants self-identified as 1 (0.66%) American Indian/Alaska Native, 21 (12.7%) Asian/Pacific Islander, 27 (16.4%) Black, non-Hispanic, 28 (17%) Hispanic, 78 (47.3%) White, non-Hispanic, 10 (6.1%) other. Reported socioeconomic status was 22 (13.3%) withheld, 53 (32.1%) did not know, 45 (27.3%) low, 13 (7.9%) moderately low, 16 (9.7%) middle, 8 (4.8%) upper middle, and 8 (4.8%) upper.

This causal-comparative and quasi-experimental, mixed-method, longitudinal study used researcher-developed web-based modules to measure Performance and Satisfaction, and used the criterion p < .05 for statistical significance. A two-way, 4 x 3 repeated measures (Time) analysis of variance (RM-ANOVA) using Preference and Content was statistically significant on each Performance measure over Time, and at two measures on Satisfaction over Time. The RM-ANOVA was statistically significant on between-subjects main effect Performance for read/write modality Content compared to aural and kinesthetic Content. There were no statistically significant main effects observed for Satisfaction. A Pearson r correlation analysis showed that participants that were older, married, and of higher socioeconomic status performed better. The correlation analysis also showed that participants who performed better reported greater likelihood to take online courses in the future, higher motivation, sufficient time and support for studies, and sufficient funding for and access to the Internet.

The study results suggested that regardless of Preference, using read/write modality Content based on the VARK model while maintaining the verbal language can yield better Performance outcomes. The study results also suggested that while maintaining the verbal language, Preference, and Content based on the VARK model do not distinguish learner Satisfaction outcomes. However, because Satisfaction has been shown to impact Performance, efficacy, and retention, it matters to educational institutions. Future research should consider more granular models and factorial research methods, because models that utilize a single representative construct score can mask effects when analyzing Performance and Satisfaction.

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  • August 2016

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  • Aug. 31, 2016, 10:41 p.m.

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  • Oct. 25, 2016, 9:45 a.m.

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Copeland, Matthew Blair. Learner Modal Preference and Content Delivery Method Predicting Learner Performance and Satisfaction, dissertation, August 2016; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc862858/: accessed June 25, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .