Brydges, Ryan

Context Assessment is central to medical education and the validation of assessments is vital to their use. Earlier validity frameworks suffer from a multiplicity of types of validity or failure to prioritise among sources of validity evidence. Kane's framework addresses both concerns by emphasising key inferences as the assessment progresses from a single observation to a final decision. Evidence evaluating these inferences is planned and presented as a validity argument. Objectives We aim to offer a practical introduction to the key concepts of Kane's framework that educators will find accessible and applicable to a wide range of assessment tools and activities. Results All assessments are ultimately intended to facilitate a defensible decision about the person being assessed. Validation is the process of collecting and interpreting evidence to support that decision. Rigorous validation involves articulating the claims and assumptions associated with the proposed decision (the interpretation/use argument), empirically testing these assumptions, and organising evidence into a coherent validity argument. Kane identifies four inferences in the validity argument: Scoring (translating an observation into one or more scores); Generalisation (using the score[s] as a reflection of performance in a test setting); Extrapolation (using the score[s] as a reflection of real‐world performance), and Implications (applying the score[s] to inform a decision or action). Evidence should be collected to support each of these inferences and should focus on the most questionable assumptions in the chain of inference. Key assumptions (and needed evidence) vary depending on the assessment's intended use or associated decision. Kane's framework applies to quantitative and qualitative assessments, and to individual tests and programmes of assessment. Conclusions Validation focuses on evaluating the key claims, assumptions and inferences that link assessment scores with their intended interpretations and uses. The Implications and associated decisions are the most important inferences in the validity argument.

Context Self‐regulated learning ( SRL ) requires an active learner who has developed a set of processes for managing the achievement of learning goals. Simulation‐based training is one context in which trainees can safely practise learning how to learn. Objectives The purpose of the present study was to evaluate, in the simulation‐based training context, the effectiveness of interventions designed to support trainees in SRL activities. We used the social‐cognitive model of SRL to guide a systematic review and meta‐analysis exploring the links between instructor supervision, supports or scaffolds for SRL , and educational outcomes. Methods We searched databases including MEDLINE and Scopus, and previous reviews, for material published until December 2011. Studies comparing simulation‐based SRL interventions with another intervention for teaching health professionals were included. Reviewers worked independently and in duplicate to extract information on learners, study quality and educational outcomes. We used random‐effects meta‐analysis to compare the effects of supervision (instructor present or absent) and SRL educational supports (e.g. goal‐setting study guides present or absent). Results From 11 064 articles, we included 32 studies enrolling 2482 trainees. Only eight of the 32 studies included educational supports for SRL . Compared with instructor‐supervised interventions, unsupervised interventions were associated with poorer immediate post‐test outcomes (pooled effect size: −0.34, p = 0.09; n = 19 studies) and negligible effects on delayed (i.e. > 1 week) retention tests (pooled effect size: 0.11, p = 0.63; n = 8 studies). Interventions including SRL supports were associated with small benefits compared with interventions without supports on both immediate post‐tests (pooled effect size: 0.23, p = 0.22; n = 5 studies) and delayed retention tests (pooled effect size: 0.44, p = 0.067; n = 3 studies). Conclusions Few studies in the simulation literature have designed SRL training to explicitly support trainees’ capacity to self‐regulate their learning. We recommend that educators and researchers shift from thinking about SRL as learning alone to thinking of SRL as comprising a shared responsibility between the trainee and the instructional designer (i.e. learning using designed supports that help prepare individuals for future learning).