The Use of a Creative Problem Solving Based Genetic Mutation Module in Higher Education
Bibliographic record
Abstract
The creative problem solving (CPS) based on genetic mutation module provides students with an opportunity to identify problems, design a problem-solving plan, choose the right path, and effectively evaluate the solution. This research aims to examine the effectiveness of CPS-based genetic mutation module to improve problem-solving skills in undergraduate students of biological education. Furthermore, the CPS module was developed on the basis of research and development (R&D) according to the Borg and Gall method and presented as a mutation module for genetic material. A group pre-test and post-test design was applied by undergraduate students of biological education at the university of Sebelas Maret in Surakarta using random sampling techniques. A total of 17 students from 5th semester were accepted as participants and treated for pre-test and post-test. The instruments used for the collection of data was an essay test design based on Polya's indicators of problem-solving skills. In addition, this module was considered as an advantage in using large database storage technologies such as NCBI and ExPASy in order to solve the problem-solving process. The application of the module has been shown to be effective in improving students' problem solving skills from a very low to a moderately high level.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".