Adaptive single case design (ASCD)

Author:

Leppink JimmieORCID

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. 

Publisher

EDIPUCRS

Subject

General Medicine

Reference17 articles.

1. 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

2. 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

3. 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

4. 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

5. 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

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