The Impact of a Novel Computer‐assisted Learning Resource on Student Learning in Undergraduate Dissection‐ and Prosection‐based Laboratory Environments
Notice bibliographique
Résumé
Human anatomy is an essential subject for the medical and health sciences. Teaching and learning human anatomy each require large investments of time and resources; however, many institutions are challenged by limitations to both. As a result, the approaches used to teach human anatomy are constantly evolving to overcome these limitations and deliver meaningful learning opportunities. One common method for enhancing anatomy education is through computer‐assisted learning (CAL). The rapid growth and advancement of technology over recent decades has made the creation of CAL resources cheaper, easier, and more accessible, facilitating their rise as popular supplements to traditional approaches such as dissection (DI) and prosection (PRO). Accordingly, this study evaluated a novel CAL resource that was created for and introduced into an undergraduate DI and PRO human anatomy course at the University of Guelph between the Fall 2015 and 2016 semesters. The objective was to determine the influence of the resource on the students' academic experiences through evaluations of their course satisfaction (CS), contextual approaches to learning (SAL, characterized by deep [DA] and surface [SA] approach scores), and overall course performance. Participants reported their demographic information, CS, SAL, and use of the CAL resource through a combination of online and written surveys. Written feedback regarding their perceptions of the CAL resource was also collected and thematic analysis was performed to extract common themes. CS was compared between the Fall 2015 and Fall 2016 academic cohorts using the Mann‐Whitney U test. Comparisons of contextual SAL and performance were made using analyses of covariance with preferred SAL scores and cumulative grade averages as covariates, respectively. CAL resource use by students in both DI and PRO was then characterized and multiple linear regression analyses were used to determine correlations between their use of the resource and DA scores, SA scores, and course performance. Although the students' mean (± SEM) CS improved between the 2015 and 2016 cohorts in both DI (2015 = 74.15 ± 3.29, 2016 = 86.54 ± 1.94, p = 0.002) and PRO (2015 = 78.41 ± 5.95, 2016 = 93.55 ± 3.06, p = 0.051), the mean differences (± SEM) in DA (DI = −2.11 ± 0.53, p < 0.0005; PRO = −2.72 ± 1.03, p = 0.010) and SA (DI = 1.95 ± 0.55, p < 0.0005; PRO = 4.16 ± 1.10, p < 0.0005) scores suggested that the course presented a more surface‐oriented environment in 2016 than in 2015. Contextual SAL and course performance were not directly influenced by CAL resource use ( p > 0.05); however, students in both DI ( p = 0.001) and PRO ( p = 0.025) who reported higher positive perceptions of the resource had higher DA scores. These findings indicated that using the CAL resource did not significantly enhance the students' learning experience. However, those who saw value in the resource and reported more positive perceptions toward it used deeper approaches to learning, which are representative of meaningful learning. Alongside the analyses of written student feedback, this suggests that the context in which CAL resources are to be disseminated may merit strong consideration before incorporating such resources into a course. 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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».