Incorporating Deep Learning and Higher Order Thinking Skills in a Large, Lecture‐based Human Physiology Course
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Notice bibliographique
Résumé
Traditionally, didactic lecture methods and multiple‐choice assessments are heavily relied upon in large classes, despite consistently being reported in literature to encourage a surface approach to learning and being limited to assessing lower order (LO) skills (Biggs & Tang, 2011). However, the perceived effort and resources required to restructure an environment such that a deep approach to learning may be encouraged often prevents instructors from altering teaching and assessment methods, particularly in large classes. The goal of this study was to determine if a large lecture‐based course (350+ students) could be modestly, but manageably, modified to support a deep approach to learning and promote the development of higher order (HO) thinking skills. Specifically, this study took place in a two‐semester Human Physiology course sequence (Phys I and Phys II) taught with instructor‐led scaffolded lectures and assessed with long‐answer written tests. It was hypothesized that this teaching and assessment structure would encourage a deep approach to learning and develop students' HO thinking skills. The Revised Two‐Factor Study Process Questionnaire (rSPQ) was administered at the beginning and end of each course to measure student approach to learning score, while student academic performance was tracked on assessment questions categorized as requiring either LO or HO thinking skills, according to the Blooming Biology Tool (BBT). Student performance on HO thinking questions remained consistent from the start to end of Phys 1 (72.9±19.4% versus 74.8±20.7%, p =0.37), but significantly improved over the course of Phys II (69.9±18.4% versus 79.4±14.8%, p <0.001). Unexpectedly, students' performance on LO thinking questions decreased in a similar pattern from the start to end of both Phys 1 (78.5±20.6% versus 69.4±17.9%, p <0.001) and Phys II (80.5±19.6% versus 72.2±24.3%, p <0.001). There was no significant change in deep or surface approach to learning scores over the course of either semester; although students consistently preferred a deep approach to a surface approach at each time point measured. Limitations regarding the tool used to measure approach to learning, combined with specific student, classroom and educational factors, may partially explain the lack of measurable changes. However, these results suggest that a large lecture‐based course which has been modestly, but manageably, modified from traditional teaching and assessment methods, can provide a learning environment which supports the maintenance of a deep approach to learning and promotes the development of HO thinking skills. Biggs JB , Tang CS , Society for Research into Higher Education . Teaching for Quality Learning at University: What the Student Does [Online]. McGraw‐Hill Education. http://search.ebscohost.com.subzero.lib.uoguelph.ca/login.aspx?direct=true&db=nlebk&AN=405333&site=ehost‐live&scope=site [12 Jul. 2018]. Support or Funding Information SSHRC Doctoral Fellowship 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.
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 | 0,008 | 0,001 |
| 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 | 0,001 | 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 découle