What’s Next? Developing Systems of Assessment for Educational Settings
Notice bibliographique
Résumé
Systems of assessment, in the form of multiple examinations, have existed at least as far back as 650 ce when the Chinese offered a battery of tests for recruiting civil servants.1 More recently, adequate performance on a variety of different assessments has been required at each step along the educational continuum to both establish competence and support learning. Although these examples constitute systems of assessment, explicit and orderly thought has not always been given to the relationships among the individual tests used in medical schools, residency, and continuing education or to the various purposes they serve. Instead, each individual assessment has been considered on its own merit, and the system has been viewed as the sum of its parts. Over the past few decades, this has begun to change. In 2001, the National Research Council recommended the development of “new systems of multiple assessments,” which it followed with a series of reports elaborating on the idea.2–4 In 2005, van der Vleuten and Schuwirth also argued for a focus on programs or systems of assessment.5 In 2012, they followed with a specific system (“programmatic assessment”) that proposed ongoing feedback and assessment with periodic decisions based on an aggregation of the data points that were available.6 Starting in 2006, this movement was nurtured by the Harvard Macy course titled “A systems approach to assessment in health professions education.” The course was designed to encourage the application of systems thinking to the development of assessment programs that support a variety of purposes including student, faculty, and program accountability and improvement.7 In 2018, a consensus framework was proposed as part of the Ottawa Conference.8 It was built on the efforts of others, and it included 7 elements of a good system of assessment: coherent, continuous, comprehensive, feasible, purpose driven, acceptable, and transparent and free from bias.2–4,9–11 The aim of this work was to offer ways of thinking about important qualities of systems of assessment that might factor into judgments about best practice. The increased emphasis on systems of assessment has occurred at a time when there have been fundamental changes to medical education. Competency- or outcomes-based education relies on the use of a variety of different methods of assessment, many of which go beyond knowledge to include skills and attitudes. At the same time, there is a need at various points in training to assess progress on the individual competencies as well as performances that require integration across them. Finally, there is growing acknowledgment of the power of formative assessment and the importance of including it in educational systems. In the context of these developments, a number of questions need to be addressed for systems of assessment in undergraduate, postgraduate, and continuing medical education. These systems are remarkable in their complexity because they need to serve multiple purposes (e.g., formative, summative) for multiple users and stakeholders (e.g., students, faculty, patients, programs, institutions, health care systems), covering multiple competencies and unfolding over time. This Commentary will touch briefly on 4 areas that require work, although there are many others as well. First, historically, virtually all assessments in education have been summative, and they have not necessarily been deployed in a coordinated fashion. Consequently, a key question for the future relates to how much summative assessment is actually needed to protect patients, establish accountability, and serve the quality control needs of educational programs. Are there ways these summative assessments can be reduced, coordinated, and/or integrated to achieve better outcomes more efficiently? Can the resources this liberates be used for other purposes within the system? Second, the recent interest in formative assessment is driven by research indicating that it has significant positive effects on learning and that it is the basis for any feedback provided to students. It faces significant challenges, however, in that it is underused, students do not necessarily value it, resource constraints often encourage the use of the same assessment for both formative and summative purposes, and faculty/institutions are neither expert in creating it nor in integrating it into their curricula, classes, and quality control systems. Underlying these challenges is a lack of research into fundamental issues. What are the effects of using the same assessment for both formative and summative purposes? What are the characteristics of effective formative assessment and the feedback provided on the basis of it? How much of what kind of formative assessment is needed and when? How can learners be motivated to use the feedback they receive? How do we train faculty to produce effective assessments of this type? Third, the individual learner is often the object of measurement in assessment today. However, an integrated system of assessment would appropriately encompass other potential targets including the curriculum, the faculty, the effectiveness of particular educational interventions, the identification of strengths and weaknesses, the team, and so on. This raises several questions including how data can be shared across these various objectives, when data can be useful for more than one purpose, when and which assessments need to be developed specifically for these “other” objects of measurement, and so on. Finally, assessment is an essential part of the training and feedback mechanisms in the complex adaptive system of medical education. It will need to support continuous monitoring of the system, rapidly signal problems, provide relevant information in a timely fashion to both users and decision makers, make efficient use of resources, and support needed change. To achieve these ends, it is necessary to apply “systems thinking” to the design of assessments. How do we bring this design approach to the currently decentralized assessment process of many educational programs and institutions? What type of expertise is needed? How do we know that the systems are working well? There are the beginnings of research, development, and experience for all these questions, and some progress has been made, but further work is needed. Ultimately, it will be useful to create a set of best practices, recognizing that they will vary by mission, context, and purpose.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,047 | 0,083 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,011 |
| Communication savante | 0,024 | 0,044 |
| Science ouverte | 0,006 | 0,013 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».