The Effectiveness of Inquiry-Based Laboratory Manual for Junior High School Biology
Bibliographic record
Abstract
This study embarked on the development of a comprehensive Junior High School Biological Inquiry Experiment Manual. This manual was utilized in a research initiative involving 30 students in the control group and an equal number in the experimental group. Rigorous data analysis techniques, encompassing mean values, standard deviations, paired t-tests, and more were employed to meticulously evaluate the efficacy of the newly developed experimental manual. The results showed that compared with the original textbooks of D Middle School, the developed Junior Middle School Biology Inquiry Laboratory Manual helps to improve students’ interest, attitude and grade performance, so the newly developed Junior High School Biology Inquiry Experiment Manual is effective. It helps to cultivate students’ core quality of Biology. In the future, the researcher may continue refining and updating the manual to ensure its effectiveness over time, such as adding visual videos of experiments, enriching inquiry experiments on human structure and function, at the same time, researchers may consider replicating this study on a larger scope by including participants from a broader spectrum, expanding the research’s scope and depth.In the transition between the old and new curriculum standards, and under the background of double reduction, this study will lay a foundation for the in-depth development of Junior High School Biology experiment textbooks.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".