Using Think‐Alouds to Explore Problem‐Solving Procedures for Anatomy Students
Notice bibliographique
Résumé
This session presents the use of think‐alouds as a powerful qualitative method to unravel student thought processes as they complete multiple‐choice assessments in anatomy. With this study, we aimed to uncover patterns in the reasoning process that students used when solving multiple‐choice questions. This required participants to verbalize their thought processes as they solved six multiple‐choice questions covering five key areas of anatomy. The multiple‐choice questions targeted three levels of cognitive functioning based off the ICE framework (Fostaty Young & Wilson, 2000), which includes recalling the fundamental facts to integrating information to creating new knowledge. Prior to the think‐alouds, the researchers designed a rubric with prompts corresponding to the different strategies that might be adopted by the students. All students were also asked to complete a practice activity in order to better understand the depth of responses that we were looking for in this study and to make students feel comfortable with this approach. One‐on‐one think aloud interviews were conducted with ten second‐year undergraduate students. Feedback from the initial individual think‐aloud sessions was used to generate a survey that was distributed across anatomy courses at Queen's University. We analyzed and categorized reasoning processes that were used by the 82 students who responded to our survey as well as those 10 students who participated in the think‐alouds. The think alouds were audio‐recorded and the qualitative content analysis protocol (Patton, 1990) was used to identify strategies that students followed when working through the questions. Amongst the challenges experienced with this approach were: students' tendencies to get distracted and go off topic; their ability to vocalize their thoughts or express limited information; finding the “appropriate” level of researcher prompting; as well as the labour intensive analysis process. We identified 16 different strategies that students used to solve multiple‐choice questions. Twelve of these have already been described and supported by the literature as procedures that learners frequently used in problem‐solving. Strategies like Checking, Clarifying, Comparing, Recalling, Relating, Predicting, and Recognizing, and Imitating are associated with the Bloom's taxonomy (Anderson and Krathwohl, 2001) and the ICE Framework (Fostaty Young & Wilson, 2000). In this session we will further describe the strategies used by the students and at the same time we will explore correlations amongst the level of the question, the strategies that were being used, and the likeliness of students getting the answer correct. We will conclude with a discussion of the challenges and benefits of using think‐alouds as a strategy for understanding and supporting student learning. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0,012 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».