A Systematic Review on the Effectiveness of Project-Based Learning for Teacher Preparation During the COVID-19 Epidemic
Bibliographic record
Abstract
The COVID-19 pandemic has had a major influence on educational institutions all across the globe, including programs that prepare teachers. Teachers in teacher preparatory programs have looked at alternative instructional methodologies including project-based learning (PBL) in response to the difficulties presented by remote learning and the lack of in-person contacts. This research seeks to evaluate the efficacy of PBL in teacher training throughout the COVID-19 pandemic using a systematic review approach. To gather data for the study, several electronic databases were searched, including Google Scholar, Scopus, Education “Research Information Center (ERIC), ProQuest, Science Direct, and Taylor & Francis Online. The researchers exclusively opted for articles listed in Scopus index, thoroughly scrutinizing them through the “Scopus resource system”. Employing Boolean search filtering and specific search strings such as “(pre-service teacher) and (project-based learning), (prospective teacher) and (project-based learning), and (teacher education) and (project-based learning)”, among others. The search eventually yielded 15 papers which were used for this review. While the participants showed a high degree of PBL utilization in their teaching methods during the pandemic, they also highlighted the value of supporting the project-based learning (PBL) environment. The results imply that PBL may have successfully train instructors for online and integrated learning settings during the COVID-19 pandemic. The findings emphasize the significance of including PBL into teacher preparation curriculum and giving teacher candidates continuous assistance and training to maximize the advantages of this teaching strategy. Further study is necessary to examine the long-term impacts of PBL on instructors’ effectiveness and student academic results after the pandemic.
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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 | 0.017 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".