MULTIPLE INTELLIGENCES AS BASIS FOR THE USE OF LEARNING STATION IN TEACHING BIOLOGY
Bibliographic record
Abstract
One of the big challenges the science education faces is identifying whether learning has really occurred when doing what is called “one-size-fits-all” type of activities. Thus, different strategies and approaches in teaching science have been proposed and used by educators to accommodate the complexity of the learners. The study covered the identification of multiple intelligences of Grade 8 students and its effect on multiple intelligence-based learning stations in teaching Biology. This study also determined to examine the efficiency and usability of Learning Station in teaching Biology and how it enhanced students’ performance. The period of research covered the fourth quarter of Grade 8 Science, School Year 2017-2018. In this view, the study employed the mixed- approach in research which comprised of descriptive and quasi-experimental one group research design. The Dominant Multiple Intelligences (MI) exhibited by Grade 8 students as assess showed that there were four (4) MI and these include Kinesthetic, Intrapersonal, Musical and Existential. More so, topics in teaching Biology 8 that were considered or the basis in using developed learning station. Furthermore, the students’ performance in every station based on their pre-test and post-test in terms of Mean Score, MPS, and SD showed that there is an increase in terms of the score from the post-test and pre-test. Considerably, the increase is significant. The assessment on comments and suggestions of the developed learning station showed that it is cost-efficient and economical, the language used is appropriate to users’ age, great number of students to execute at a time and easy to store and transport, and have maximum collaboration on shared products, and activities appropriate on classroom size.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".