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A conceptual model for students' satisfaction with team-based learning using partial least squares structural equation modelling in a faculty of life sciences, in the United Kingdom.

ABSTRACT: PURPOSE:Students' satisfaction is an essential element in higher education. This study aimed to identify paths and predictive power of students' satisfaction during team-based learning (TBL) activities in the faculty of life sciences using partial least squares structural equation modelling (PLS-SEM). METHODS:In 2018-2019, at the University of Sussex (Falmer, UK), 180 life science students exposed to TBL were invited to participate in the study. Team-Based-Learning-Student-Assessment-Instrument was used. A conceptual model was developed for testing six hypotheses. H1: What was the effect of TBL on student satisfaction? H2: What was the effect of lectures on student satisfaction? H3: What was the effect of TBL on accountability? H4: What was the effect of lectures on accountability? H5: What was the effect of accountability on student satisfaction? H6: What were the in-sample and out-of-sample predictive power of the model? The analysis was conducted using the PLS-SEM approach. RESULTS:Ninety-nine students participated in the study giving a 55% response rate. Confirmatory tetrad analysis suggested a reflective model. Construct reliability, validity, average extracted variance, and discriminant validity were confirmed. All path coefficients were positive, and 5 were statistically significant (H1: ?=0.587, P<0:001; H2: ?=0.262, P<0.001; H3: ?=0.532, P<0.001; H4: ?=0.063, P=0.546; H5: ?=0.200, P=0.002). The in-sample predictive power was weak for Accountability, (R2=0.303; 95% confidence interval [CI], 0.117-0.428; P<0.001) and substantial for Student Satisfaction (R2=0.678; 95% CI, 0.498-0.777; P<0.001). The out-of-sample predictive power was moderate. CONCLUSION:The results have demonstrated the possibility of developing and testing a TBL conceptual model using PLS-SEM for the evaluation of path coefficients and predictive power relative to students' satisfaction.

SUBMITTER: Manfrin A

PROVIDER: S-EPMC6887653 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A conceptual model for students' satisfaction with team-based learning using partial least squares structural equation modelling in a faculty of life sciences, in the United Kingdom.

Manfrin Andrea A Apampa Bugewa B Parthasarathy Prabha P

Journal of educational evaluation for health professions 20191113

<h4>Purpose</h4>Students' satisfaction is an essential element in higher education. This study aimed to identify paths and predictive power of students' satisfaction during team-based learning (TBL) activities in the faculty of life sciences using partial least squares structural equation modelling (PLS-SEM).<h4>Methods</h4>In 2018-2019, at the University of Sussex (Falmer, UK), 180 life science students exposed to TBL were invited to participate in the study. Team-Based-Learning-Student-Assessm ...[more]

PMID: 31791118

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Project description:BackgroundThe literature has identified various factors that promote or hinder people's intentions towards COVID-19 vaccination, and structural equation modelling (SEM) is a common approach to validate these associations. We propose a conceptual framework called social media infodemic listening (SoMeIL) for public health behaviours. Hypothesizing parameters retrieved from social media platforms can be used to infer people's intentions towards vaccination behaviours. This study preliminarily validates several components of the SoMeIL conceptual framework using SEM and Twitter data and examines the feasibility of using Twitter data in SEM research.MethodsA total of 2420 English tweets in Toronto or Ottawa, Ontario, Canada, were collected from March 8 to June 30, 2021. Confirmatory factor analysis and SEM were applied to validate the SoMeIL conceptual framework in this cross-sectional study.FindingsThe results showed that sentiment scores, the log-numbers of favourites and retweets of a tweet, and the log-numbers of a user's favourites, followers, and public lists had significant direct associations with COVID-19 vaccination intention. The sentiment score of a tweet had the strongest relationship, whereas a user's number of followers had the weakest relationship with the intention of COVID-19 vaccine uptake.InterpretationThe findings preliminarily validate several components of the SoMeIL conceptual framework by testing associations between self-reported COVID-19 vaccination intention and sentiment scores and the log-numbers of a tweet's favourites and retweets as well as users' favourites, followers, and public lists. This study also demonstrates the feasibility of using Twitter data in SEM research. Importantly, this study preliminarily validates the use of these six components as online reaction behaviours in the SoMeIL framework to infer the self-reported COVID-19 vaccination intentions of Canadian Twitter users in two cities.FundingThis study was supported by the 2023-24 Ontario Graduate Scholarship.

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A conceptual model for students' satisfaction with team-based learning using partial least squares structural equation modelling in a faculty of life sciences, in the United Kingdom.

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A conceptual model for students' satisfaction with team-based learning using partial least squares structural equation modelling in a faculty of life sciences, in the United Kingdom.

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