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Record W4387562833 · doi:10.1002/jdd.13391

Interactive learning content to supplement didactic lectures in dental education

2023· article· en· W4387562833 on OpenAlexaffabout
Nazlee Sharmin, Janki Pandya, Thomas R. Stevenson, A. Chow

Bibliographic record

VenueJournal of Dental Education · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSentenceTable of contentsComputer scienceMathematics educationMultimediaPsychologyWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.063
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0630.017

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.391
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2023
Admission routes2
Has abstractyes

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