Challenges and Strategies Adopted for Remote Teaching of Biochemistry During the COVID-19 Pandemic: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: In March 2020, the global landscape witnessed widespread upheavals in both socioeconomic and educational spheres due to the onset of the COVID-19 pandemic. With measures imposed to control the virus's spread, educational institutions around the world embraced digital learning, introducing challenges in the adaptation to virtual education. This shift proved especially daunting in resource-limited nations with limited digital infrastructure. OBJECTIVE: This scoping review aims to explore the experiences of biochemistry educators during the COVID-19 pandemic, focusing on successful pedagogical strategies used to overcome challenges in remote teaching. The goal is to compile valuable information applicable to health-related undergraduate and postgraduate courses. METHODS: This review considers studies and experiences related to the transition to remote biochemistry education during the pandemic. It encompasses a variety of pedagogical approaches, including online teaching tools, interactive methods, and alternatives to practical laboratory classes. The search spans databases such as MEDLINE, the Cochrane Database of Systematic Reviews, and Joanna Briggs Institute (JBI) Evidence Synthesis, with a focus on identifying systematic or scoping reviews; however, none were identified in the preliminary search. RESULTS: Starting in February 2022, the scoping review protocol was scheduled for completion by July 2024. From an initial pool of 1171 results, 85 articles were selected, with duplicate verification pending for the subsequent phase of the project. The findings from this review on biochemistry teaching strategies will be communicated using a combination of descriptive narrative, graphical, and tabular formats, emphasizing diverse pedagogical approaches pertinent to the subject. Dissemination will occur through regional and national scientific conference presentations, alongside publication in a peer-reviewed journal. CONCLUSIONS: This review aims to generate innovative pedagogical approaches and pinpoint learning activities, materials, and tools that support social and collaborative learning across various subjects, including biochemistry. Moreover, it will offer perspectives from students and educators on the implemented activities, with the intention of integrating them as supplementary methods to boost student participation, and thereby, improve learning outcomes and skill development. TRIAL REGISTRATION: Open Science Framework VZSA7; https://osf.io/VZSA7/. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59552.
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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 | 0.105 | 0.119 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.011 |
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".