Development and validation of the student ratings in clinical teaching scale in Australia: a methodological study
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Purpose This study aimed to devise a valid measurement for assessing clinical students’ perceptions of teaching practices. Methods A new tool was developed based on a meta-analysis encompassing effective clinical teaching-learning factors. Seventy-nine items were generated using a frequency (never to always) scale. The tool was applied to the University of New South Wales year 2, 3, and 6 medical students. Exploratory and confirmatory factor analysis (exploratory factor analysis [EFA] and confirmatory factor analysis [CFA], respectively) were conducted to establish the tool’s construct validity and goodness of fit, and Cronbach’s α was used for reliability. Results In total, 352 students (44.2%) completed the questionnaire. The EFA identified student-centered learning, problem-solving learning, self-directed learning, and visual technology (reliability, 0.77 to 0.89). CFA showed acceptable goodness of fit (chi-square P<0.01, comparative fit index=0.930 and Tucker-Lewis index=0.917, root mean square error of approximation=0.069, standardized root mean square residual=0.06). Conclusion The established tool—Student Ratings in Clinical Teaching (STRICT)—is a valid and reliable tool that demonstrates how students perceive clinical teaching efficacy. STRICT measures the frequency of teaching practices to mitigate the biases of acquiescence and social desirability. Clinical teachers may use the tool to adapt their teaching practices with more active learning activities and to utilize visual technology to facilitate clinical learning efficacy. Clinical educators may apply STRICT to assess how these teaching practices are implemented in current clinical settings.
Abstract
Purpose This study aimed to devise a valid measurement for assessing clinical students’ perceptions of teaching practices. Methods A new tool was developed based on a meta-analysis encompassing effective clinical teaching-learning factors. Seventy-nine items were generated using a frequency (never to always) scale. The tool was applied to the University of New South Wales year 2, 3, and 6 medical students. Exploratory and confirmatory factor analysis (exploratory factor analysis [EFA] and confirmatory factor analysis [CFA], respectively) were conducted to establish the tool’s construct validity and goodness of fit, and Cronbach’s α was used for reliability. Results In total, 352 students (44.2%) completed the questionnaire. The EFA identified student-centered learning, problem-solving learning, self-directed learning, and visual technology (reliability, 0.77 to 0.89). CFA showed acceptable goodness of fit (chi-square P<0.01, comparative fit index=0.930 and Tucker-Lewis index=0.917, root mean square error of approximation=0.069, standardized root mean square residual=0.06). Conclusion The established tool—Student Ratings in Clinical Teaching (STRICT)—is a valid and reliable tool that demonstrates how students perceive clinical teaching efficacy. STRICT measures the frequency of teaching practices to mitigate the biases of acquiescence and social desirability. Clinical teachers may use the tool to adapt their teaching practices with more active learning activities and to utilize visual technology to facilitate clinical learning efficacy. Clinical educators may apply STRICT to assess how these teaching practices are implemented in current clinical settings.
참고문헌 (15)
- Eggleton K, "A psychometric evaluation of the University of Auckland General Practice Report of Educational Environment : UAGREE", Educ Prim Care, 28, pp.86~93 (2017)[DOI]
- Huang PH, "A meta-analysis for comparing effective teaching in clinical education", Med Teach, 41, pp.1129~1142 (2019)[DOI]
- Brown G, "The SAGE handbook of survey development and application", pp.120~131 (2023), Sage Publications
- Kyriazos TA, "Applied psychometrics : sample size and sample power considerations in factor analysis(EFA, CFA)and SEM in general", Psychology, 9, pp.2207~2230 (2018)[DOI]
- Kang Y, "The role of measurement quality on practical guidelines for assessing measurement and structural invariance", Educ Psychol Meas, 76, pp.533~561 (2016)[DOI]
- Byrne BM, "Structural equation modeling with AMOS: basic concepts, applications, and programming", pp.460~ (2016), Routledge
- McCabe A, "Student-centred learning : the role and responsibility of the lecturer", Teach High Educ, 19, pp.350~359 (2014)[DOI]
- Mehta NB, "Just imagine : new paradigms for medical education", Acad Med, 88, pp.1418~1423 (2013)[DOI]
- Wang M, "Connecting problem-solving and knowledge-construction processes in a visualization-based learning environment", Comput Educ, 68, pp.293~306 (2013)[DOI]
- Weller J, "Building the evidence on simulation validity : comparison of anesthesiologists’ communication patterns in real and simulated cases", Anesthesiology, 120, pp.142~148 (2014)[DOI]
- Burn K, "A review of ‘research-informed clinical practice’ in initial teacher education", Oxf Rev Educ, 41, pp.217~233 (2015)[DOI]
- Ge X, "Guest editors’ introduction : special issue on problem-based learning in health professions education", toward advancement of problem-based learning research and practice in health professions education : motivating learners, facilitating processes, and supporting with technology, 2015, pp.9, 5~
- Carr SE, ""Helping someone with a skill sharpens it in your own mind" : a mixed method study exploring health professions students experiences of Peer Assisted Learning(PAL)", BMC Med Educ, 16, pp.48~ (2016)[DOI]
- Akhigbe T, "Reflection and reflective practice in general practice : a systematic review exploring and evaluating key variables influencing reflective practice", J Adv Med Med Res, 34, pp.34~44 (2022)[DOI]
- Rodino AM, "Assessing preceptor use of cognitive apprenticeship : is the Maastricht Clinical Teaching Questionnaire(MCTQ)a useful approach", Teach Learn Med, 31, pp.506~518 (2019)[DOI]