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Abstract
PurposeStudents’ 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).MethodsIn 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.ResultsNinety-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.ConclusionThe 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.
Highlights
Team-Based Learning (TBL) is an evidence-based collaborative learning and teaching strategy designed around units of instruction, known as “modules,” that are taught in a three-step cycle: preparation, in-class readiness assurance testing, and application-focused exercise
H1: What was the effect of team-based learning (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 partial least squares structural equation modelling (PLS-Structural equation modelling (SEM)) approach
Hypothesis 1 (H1): What was the effect of TBL on student satisfaction? Hypothesis 2 (H2): What was the effect of lectures on student satisfaction? Hypothesis 3 (H3): What was the effect of TBL on accountability? Hypothesis 4 (H4): What was the effect of lectures on accountability? Hypothesis 5 (H5): What was the effect of accountability on student satisfaction? Hypothesis 6 (H6): What were the in-sample and out-of-sample predictive power of the model? Study power A post hoc power calculation was conducted using G*Power version 3.1.9.3 [9]
Summary
Team-Based Learning (TBL) is an evidence-based collaborative learning and teaching strategy designed around units of instruction, known as “modules,” that are taught in a three-step cycle: preparation, in-class readiness assurance testing, and application-focused exercise. Partial least squares structural equation modelling (PLS-SEM) is a prediction-oriented variance-based approach that focuses on endogenous target constructs in the model and aims at maximising their explained variance (e.g. looking at the coefficient of determination (R2) value) [2]. A few studies conducted in the United Kingdom (UK) analysed the use of TBL with the team-based learning students assessment instruments (TBL-SAI) [5,6]. To the best of our knowledge PLS-SEM has not been used to evaluate students’ accountability, preference for TBL or lectures and satisfaction as measured using the TBL-SAI in the United Kingdom. Purpose: It aimed to identify paths and predictive power of students’ satisfaction during team-based-learning activities in the faculty of life sciences using PLS-SEM
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