Improved Conceptual Understanding in Learning Biology through Local-ized and Contextualized Learning Activities
Bibliographic record
Abstract
Nowadays, a relatively large number of high school students have poor learning performance in Science which affects their academic performance and even the school National Achievement Test (NAT) result. This study determined the effectiveness of the localized and contextualized learning activities in improving the conceptual understanding of tenth-grade students in learning Biology for the Third Quarter of the School Year 2019-2020. The study was conducted using the pretest-posttest quasi-experimental design involving sixty (60) student-participants in a public secondary school in the Division of Surigao del Sur, Mindanao, Philippines. This study employed an intact group sampling technique in selecting the participants into two groups, the control group (conventional learning material/learning activity sheets) and the treatment group (localized and contextualized learning activities). The findings of the study revealed that the post-test mean score of the treatment group is greater than the post-test result of the control group. Also, there was a significant difference in the conceptual understanding of the students in the control group and treatment group. It shows that localized and contextualized learning activities promote a better understanding of the lesson. Hence, future study is suggested to develop and produce localized and contextualized instructional materials such as modules and worksheets for other learning competencies in Science.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".