An Interactive Feedback Model for Enhancing Teaching and Learning in Dental and Undergraduate Medical Science Education
Bibliographic record
Abstract
Introduction and Objective As higher education institutions transitioned to fully online teaching and learning environments due to the Covid‐19 pandemic, the importance of effective pedagogical models was reinforced. This unexpected and abrupt change of curriculum delivery from previously well‐established educational programs has not been without challenges, and subsequently identified a need for improvements in online teaching and learning practices. In September of 2020, a group of dental students and motivated faculty at Western University Schulich School of Medicine and Dentistry came together in response to the pandemic and established an online feedback model (V1). This feedback model created the foundation for a Students as Partners program (SaP). SaP programs provide students and faculty the opportunity to foster academic partnerships and together be part of the teaching and learning process. This program allowed dentistry students to provide real‐time feedback on curricular content and delivery as teaching and learning shifted online. In order to evaluate the impact a real‐time feedback model has in enhancing teaching and learning, it is crucial to determine whether the feedback model closes the feedback loop. Closing the feedback loop is a systematic process where student feedback is shared with students and faculty, timely actions are taken to implement changes from the student voice, and the effectiveness of actioned improvements is monitored. Materials and Methods To assess if the V1 feedback system closes the feedback loop to enhance teaching and learning, we distributed Qualtrics surveys with 7‐point Likert scale statements and free‐text response questions to obtain student and faculty perspectives on their awareness, use, and perceived effectiveness of the V1 feedback system. Results After collecting pilot data from students and faculty on their perspectives of the V1feedback system, changes were made to the model of SaP and essential elements were re‐established. The student perspective reported that 73% (n=11) of students disagreed that they were well aware of the V1 feedback system and its intended use, 78% (n=10) disagreed that the V1 feedback system provided a solution for real‐time improvements, and 80% (n=5) disagreed that they were notified of changes being made to the feedback they provided. These results identified closing the feedback loop was not taking place in this V1 model. Conclusion Since increasing the awareness of the SaP program, an updated feedback system (V2) that closes the feedback loop to enhance teaching and learning in dental and undergraduate medical science education was developed and is being evaluated. Further analysis will determine if the V2 feedback system closes the feedback loop in a real‐time feedback model. Significance This research is intended to assist researchers in dental and undergraduate medical science education with a feedback model that elicits student voices while enhancing the teaching and learning experience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".