Evaluation of Basic Science Independent Learning Modules Implemented in a First Year Medical School Curriculum
Notice bibliographique
Résumé
Introduction Medical education is shifting to a competency‐based education (CBE) model on a global scale. The Schulich School of Medicine & Dentistry implemented a renewed curriculum for the 2019–2020 academic year, adopting a CBE model at the undergraduate medical education (UME) level. One of the key design principles of CBE is to develop a learner‐centred environment that fosters active learning. Active learning modalities often require more content delivery outside of the classroom via online independent learning modules (ILs). Literature on the implementation and evaluation of ILs within an UME integrative curriculum is limited and conflicting, highlighting the need for the investigation of implementation strategies and evaluations of ILs. The purpose of this project has two aims: i) determine which IL format, Articulate Rise 360 vs. Articulate Storyline 360, first‐year medical students perceive to be more effective to enhance their learning; ii) determine which elements of each learning module format are perceived to be more effective at fostering learning. Methods Basic science ILs implemented in a Foundations of Medicine course were evaluated. After each learning module students were given the opportunity to provide feedback on each IL via an anonymous questionnaire. The questionnaire consisted of 6 Likert scale questions, two fill in the blank questions, and two short answer questions. Likert data were analyzed using unpaired 2‐tailed t‐tests and short answer data will be coded and themed for analysis. Results Preliminary data indicate that Articulate Rise 360 and Articulate Storyline 360 scored similarly on Likert scales with respect to visual design, navigation, learning objective achievement, and interactivity. Articulate Storyline 360 scored significantly higher than Articulate Rise 360 on the Likert scale question evaluating the effectiveness of the embedded knowledge checks with 69.9% of students indicating that knowledge checks in Storyline 360 ILs were “Very valuable” or “Extremely valuable”, compared to 47.9% for the Rise 360 modules. When analyzing the ILs based on length of completion, students perceived ILs that took under 60 minutes to complete to have significantly more effective visual designs, more relevant examples, and enable achievement of learning objectives when compared to ILs that take over 60 minutes to complete. The short answer data has yet to be analyzed. Discussion First year medical students preferred knowledge checks in Articulate Storyline 360 ILs. This may be due to the enhanced interactivity of Storyline 360. Rise 360 is more ridged in its structure and its ability to create interactive elements, whereas Storyline 360 has an enhanced ability to build a variety of question formats. Data indicates that students feel shorter ILs are more effective at accomplishing most or all learning objectives. This may be due to students losing focus or being overwhelmed with extraneous information with longer ILs. Preliminary results suggest that faculty creating new ILs should limit the length to under 60 total minutes and utilize a software that allows for a variety of knowledge checks. Qualitative analysis of the short answer questions will enable further investigation into student preferences of IL attributes.
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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 | 0,008 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».