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

• Main Authors: Andrea Manfrin, Bugewa Apampa, Prabha Parthasarathy
• Format: Article
• Language: English
• Published: Korea Health Insurance Licensing Examination Institute 2019-11-01
• Series: Journal of Educational Evaluation for Health Professions
• Subjects: least-squares analysis; personal satisfaction; problem-based learning; students; united kingdom
• Online Access: http://www.jeehp.org/upload/jeehp-16-36.pdf

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: β=0.587, P<0:001; H2: β=0.262, P<0.001; H3: β=0.532, P<0.001; H4: β=0.063, P=0.546; H5: β=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.