Application of PBL Using Laboratory and Mathematical Thinking Ability to Learning Outcomes of General Chemistry Course
Bibliographic record
Abstract
The abundance of chemical concepts and general chemistry lessons that tend to be monotonous and have not yet considered the ability of mathematical thinking to cause problems in learning that resulted in low student learning outcomes. The purpose of this research is to know the influence of learning model and mathematical thinking ability toward General Chemistry study result, also interaction between learning model through laboratory usage and mathematical thinking ability. This research is an experimental research using PBL and DI model. The learning result data is obtained from general chemistry study result test and mathematical thinking ability data is obtained through the test of mathematical thinking ability which has all been validated. The data analysis technique used two way analysis of variance (ANOVA). The result of the research shows that there is a significant effect of the learning model on the students ‘learning outcomes in the General Chemistry course and there is interaction between the learning model using the laboratory with the ability to think mathematically on the students’ General Chemistry students learning outcomes. This research concludes that the PBL model using laboratories is well used in general chemistry learning, and preferably in teaching general chemistry courses, the lecturer considers students’ mathematical thinking skills.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".