Interactive learning content to supplement didactic lectures in dental education
Notice bibliographique
Résumé
Students studying in Dentistry and Dental Hygiene Programs receive a significant portion of their dental education through didactic lectures. Although the recent rise of curricular transformations in dental schools aims to improve students’ learning experiences by introducing innovative teaching techniques and technologies,1, 2 in most cases, the didactic lectures remain in their traditional format, lacking opportunities for active learning. We have created a series of interactive HTML5 learning content using H5P to supplement foundational science didactic lectures in the Doctor of Dental Surgery (DDS) program at the University of Alberta. H5P is a plugin tool that facilitates the creation and distribution of various interactive learning content. Three types of HTML5 content were created for DDS students: (i) Drag and Drop images or words; (ii) Fill in the Blank by dragging the words; and (iii) Dialogue Card. “Drag and Drop” activities enable students to drag items (text or images) and drop them in their correct position to get scored (Figure 1A,B). Fill in the Blank activities let students complete a sentence by dragging the appropriate words from a given set of “word collections” (Figure 1C,D). Dialogue Cards are two-sided flashcards that allow students to practice and strengthen their knowledge of concepts. One side of the digital flashcard contains a question, image, and /or key concepts. The other side of the same card has the answer to the question. Students can turn the card by clicking it and report if the answer was correct or incorrect. The Dialogue Card activity keeps track of the correct and incorrect answers. In the next turn, the cards with previous incorrect performance will appear more frequently than the accurate performance (Figure 2). First-year DDS students were invited to participate in a survey to assess (i) the perceived impact of H5P content on students’ learning and (ii) what features of H5P content are most appreciated by the students. The Research Ethics Board of the University of Alberta has approved this study (ID: Pro00117742). Forty-seven percent of the first-year dentistry class (n = 15) participated in the voluntary anonymous survey. Hundred percent of the participants agreed or strongly agreed that the H5P content made learning easier and enjoyable and impacted the learning experience in a positive way (Figure 3A). All participants affirmed that the H5P contents enabled them to self-assess their learning and would like to see similar content in other aspects of their studies. The features of H5P content most appreciated by the students were that they helped them self-assess their learning, were interactive, and helped clarify concepts (Figure 3B). Descriptive student comments also showed their enthusiasm and willingness to have similar interactive content in other aspects of their learning (Figure 3C). Technology in didactic teaching promotes active learning and student engagement.3 We have identified the benefits of incorporating interactive HTML5 content in didactic learning to make education more enjoyable and interactive for dental students. The authors of this study declared no conflict of interest. This work was supported by a School of Dentistry Education Research Fund grant.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,063 | 0,017 |
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 ».