Adaptive single case design (ASCD)

A model for education, training, and assessment

Keywords: single case design, adaptive single case design, aprendizado, inovação pedagógica

Abstract

Aims: single case designs (SCDs) can help us understand change in learning-related variables, such as knowledge and skill, at the level of an individual learner, at the level of a team or group of learners, or at the level of a situation or system. Adaptive single case design (ASCD) is a new model that integrates (i.) elements of methods of education, training, and assessment that, through research methods other than SCDs, have received solid empirical evidence in the research literature and (ii.) principles of SCDs that can facilitate the integration of research in everyday practice. The rationale behind ASCD is to allow rapid evidence-based decision making in the practice of education, training, and assessment, at the unit of analysis – individual, group, team, situation, or system – that is considered appropriate in the context at hand. 
Method: an ASCD algorithm is introduced and discussed in the context of change at the level of the individual, change in a group or team, and change in a situation or system. 
Results: ASCD can be used to understand change at each of the previously mentioned units of analysis at any number of units including a single unit (one individual, one team, or one situation or system), and this change can be used for research purposes as well. 
Conclusion: ASCD enables both evidence-based practical decision making and research without stringent demands on the number of learners, groups, teams, situations, or systems. 

Downloads

Download data is not yet available.

Author Biography

Jimmie Leppink, Hospital Virtual Valdecilla (HvV), Santander, Cantabria, Spain.

PhD in Statistics Education, LLM in Forensics, Criminology and Law, and MSc in Psychology and Law from Maastricht University, the Netherlands; MSc in Statistics from Catholic University of Leuven, Belgium; currently Research Director at Hospital Virtual Valdecilla (HvV), in Santander, Spain.

References

Nickson CP, Petrosoniak A, Barwick S, Brazil V. Translational simulation: from description to action. Adv Simul. 2021;6(6). https://doi.org/10.1186/s41077-021-00160-6

Van de Schoot R, Miocevic M. Small sample size solutions: a guide for applied researchers and practitioners [Internet]. New York: Routledge; 2020 [cited 2021 Dec 2]. Available from: https://library.oapen.org/handle/20.500.12657/22385

Leppink J. Small numbers are an opportunity, not a problem. Sci Med. 2021;31(1): e40128. https://doi.org/10.15448/1980-6108.2021.1.40128

Pinheiro J, Bates D, DebRoy S, Sarkar D, Team RC. nlme: linear and nonlinear mixed effects models, R Project: 2021; version 3.1-152 [Internet]. [cited 2021 Dec 2]. Available from: https://cran.r-project.org/web/packages/nlme/nlme.pdf

R Core Team. R: a language and environment for statistical computing. R Project: 2021; version 4.0.5 [Internet]. [cited 2021 Dec 2]. Available from: https://www.r-project.org

Leppink J. Assessment of individual competence: a sequential mixed model. Sci Med. 2021;31(1): e41736. https://doi.org/10.15448/1980-6108.2021.1.41736

Brazil V. Translational simulation: not ‘where?’ but ‘why?’ A functional view of in situ simulation. Adv Simul. 2017;2:20. https://doi.org/10.1186/s41077-017-0052-3

Leppink J, Van den Heuvel A. The evolution of cognitive load theory and its application to medical education. Perspect Med Educ. 2015;4(3):119-27. https://doi.org/10.1007/s40037-015-0192-x

Frerejean J, Van Merriënboer JJG, Kirschner PA, Roex A, Aertgeerts B, Marcellis M. Designing instruction for complex learning: 4C/ID in higher education. Eur J Educ. 2019;54(4):513-24. https://doi.org/10.1111/ejed.12363

Roussin CJ, Weinstock P. SimZones: an organizational innovation for simulation programs and centers. Acad Med. 2017;92(8):1114-20. https://doi.org/10.1097/ACM.0000000000001746

Bjork RA, Dunlosky J, Kornell N. Self-regulated learning: beliefs, techniques, and illusions. Ann Rev Psych. 2013;64:417-44. https://doi.org/10.1146/annurev-psych-113011-143823

Cheng A, Eppich W, Epps C, Kolbe M, Meguerdichian M, Grant V. Embracing informed learner self-assessment during debriefing: the art of plus-delta. Adv Sim. 2021;6:22. https://doi.org/10.1186/s41077-021-00173-1

Rudolph JW, Simon R, Raemer DB, Eppich WJ. Debriefing as formative assessment: closing performance gaps in medical education. Acad Emerg Med. 2008;15(11):1010-6. https://doi.org/10.1111/j.1553-2712.2008.00248.x

Schuwirth LWT, Van der Vleuten CPM. Programmatic assessment: from assessment of learning to assessment for learning. Med Teach .2011;33(6):478-85. https://doi.org/10.3109/0142159X.2011.565828

Leppink J. The art of modelling the learning process: uniting educational research and practice. Cham: Springer; 2020. https://doi.org/10.1007/978-3-030-43082-5

Leppink J. Article numbers as a leading indicator of publication time. Sci Med. 2021;31(1):e41065. https://doi.org/10.15448/1980-6108.2021.1.41065

Leppink J, Maestre JM, Rojo E, Del Moral I. Simulation and practice: a repeated measurements perspective. Rev Esp Educ Med [Internet]. 2021 [cited 2021 Dec 2];2(2). Available from: https://doi.org/10.6018/edumed.487211

Published
2022-07-08
How to Cite
Leppink, J. (2022). Adaptive single case design (ASCD): A model for education, training, and assessment. Scientia Medica, 32(1), e42370. https://doi.org/10.15448/1980-6108.2022.1.42370
Section
Education in Health Sciences