Strategic Intervention Material-Based Instruction, Learning Approach and Students‘ Performance in Chemistry
Bibliographic record
Abstract
Abstract. This study explored the learning approach adopted and attempted to investigate the effect of Strategic Intervention Material-Based Instruction (SIM-BI) on the performance of students in high school Chemistry. It utilized the pretest-posttest pre-experimental design. The SIM-BI used as a treatment of the study covered one of the least mastered skills in the subject area which is chemical bonding. Two classes of 80 students enrolled in Chemistry during the fourth quarter of the school year 2012-2013 were used as respondents. They were classified according to their learning approaches which were on their mean scores in the Chemistry Learning Approach Inventory (CLAI). The score in the Chemistry Achievement Test (CAT) administered as pretest and posttest measured students ‘ performance in Chemistry. Dependent t-test was employed to determine the significant difference between the mean responses in the pretest and posttest. Results of the study showed that the use of SIM-BI is effective in terms of improving students‘ performance and learning approach. The surface learners performed equally well as the deep learners when SIM-BI was used. The positive result of the survey suggested that the SIM was appreciated and appealed to both types of learners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".