The Implementation of Team-Based Discovery Learning to Improve Students’ Ability in Writing Research Proposal
Bibliographic record
Abstract
Writing research proposal in educational setting is a very complex process involving variety of elements. Consequently, analyzing the complex elements from introduction to data analysis sections in order to yield convinced research proposal writing through reviewing reputable journal articles is worth-contributing. The objectives of this research are to improve students’ ability in generating a research topic from reputable journal articles, developing thesis proposal draft, and writing comprehensive thesis proposal. A classroom action research administered at English Department University of Muhammadiyah Gresik Indonesia is adopted. The results reveal that the implementation of team-based discovery learning may improve students’ ability in generating a research topic, developing research proposal draft and writing comprehensive research proposal. Several suggestions are addressed. First, although the syntax of the team based discovery learning is quite similar to the remaining strategies but it will not work more optimally if it is not followed by relevant sets of guiding questions reflecting the detailed content of each reputable journal article in each meeting. Second, learning innovations activities through intensive writing practices and consultations should be taken into account to foster the steps of discovery learning in group discussion process. Finally, the results of commonalities of strategies may be used as a reference to enhance students’ ability in writing comprehensive research proposal.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".