Action research with projects to facilitate students to study research and prepare research proposals during the Covid-19 pandemic
Bibliographic record
Abstract
Knowledge and skills in the field of research are key requirements for the successful completion of studies for students, including prospective teachers. However, the outbreak of the COVID-19 coronavirus has made it difficult for students to conduct research in the usual ways. This study aims to apply project-based learning through action research to teach students educational research methods and to help them prepare research plans that are adaptive to classroom situations. This is achieved through the provision of learning resources to teach research theory, followed by the implementation of action research to write research proposals and evaluations of learning outcomes. Learning resources were systematically arranged to support online learning to teach research methods, and effective action research led students to learn actively and develop educational research plans. Competence in the field of research was achieved. Student learning outcomes sequentially for assignment scores (M = 88.38 ± 3.00), final project scores (M = 88.20 ± 3.55), and posttest (M = 92.06 ± 2.17) were all high. Project-based learning is shown to be effective in guiding students to learn actively by utilizing available learning resources. It motivates students to learn independently and can be applied to achieve competency targets in both normal and abnormal learning situations, such as those experienced during the COVID-19 pandemic.
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 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 | 0.032 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".