Community College Anatomy and Physiology Education Research (CAPER): Can Educational Research Drive Pedagogical Change?
Notice bibliographique
Résumé
Human Anatomy and Physiology (A&P) is a required course for many community college (CC) students aiming for careers in health sciences. CC instructors face heavy workloads and few opportunities for professional development. Students face heightened academic and non‐academic challenges which can lead to debilitating anxiety. Traditional instructor‐centered teaching strategies predominate. The result is predictable: an environment where there is high instructor burnout and high student attrition rates. Transitioning to more evidence‐based instructional practices (EBIPs) has been shown to promote student learning. Despite the potential positive impact of this change on CC institutions and their students, widespread adoption of student‐centered strategies remains elusive. Evidence shows change requires more than reading journal articles or attending workshops. The Community College Anatomy and Physiology Education Research (CAPER) project takes an evidence‐based teaching approach to fostering transformation. In each year of the two year project, six CC A&P instructors (two from each of three schools) combine a professional development course with the design, implementation, and dissemination of a small‐scale educational research project investigating the impact of a student‐centered teaching strategy on learning and anxiety. We are currently in year 1, and six CC instructors have completed the professional development course and project proposals and are implementing their research projects. Participants often assumed that they were required to develop a novel and innovative project using gold‐standard experimental designs and quantitative analyses, and were skeptical about the utility of qualitative measures and experimental designs not involving control groups. In addition to providing access to external experts in qualitative and quantitative analysis, we emphasized that participants could make a contribution to the field by following one of two approaches. First, they could look at less well‐understood impacts of an established EBIP, such as science anxiety, or attempt to validate the effectiveness of an EBIP in on the community college student population. Alternatively, they could investigate a newly developed teaching practice using well‐established data collection methods with the intent of possibly identifying a new EBIP. While an important goal of CAPER is to produce publishable data regarding the efficacy of EBIPs in CCs, an equally important goal is pedagogical transformation. Thus, softening the rigor of experimental design for our target audience of community college instructors may actually promote scholarly teaching. Data that has a larger noise‐signal ratio than what would be acceptable in traditional research domains may still have a place in educational research, by providing an achievable target for potential novice educational researchers. Support or Funding Information This grant is supported by NSF grant #1829157. 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 | 0,133 | 0,256 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,017 |
| Communication savante | 0,021 | 0,014 |
| Science ouverte | 0,004 | 0,011 |
| Intégrité de la recherche | 0,005 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
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 ».