The Development of a Blended Instructional Model Using Problem-Based Learning with Graphic Organizers to Enhance Systems Thinking Skills in Computational Science for Students in Lower Secondary School
Bibliographic record
Abstract
The research objectives were to 1) study the current conditions, problems, and good practices regarding teaching and learning 2) develop a blended instructional model using problem-based learning with graphic organizers to enhance systems thinking skills in computational science for students in lower secondary school and 3) study the results of using the instructional model. The research and development process was divided into 3 phases. Phase 1; study the current conditions, problems, and good practices regarding teaching and learning, the sample group included 368 teachers and 11 teachers. Phase 2; develop a blended instructional model, the sample consisted of 8 and 7 experts, and Phase 3; study the results of using the instructional model, the sample consisted of 35 grade 7 students. Data were analyzed using basic statistics and hypothesis testing statistics. The research results found that 1) Current conditions and overall problems were moderate level. The good practices included blended learning methods, problem-based learning, and instructional media with graphic organizers 2) The blended instructional model using problem-based learning along with graphic organizers included 4 core components: principles, objectives, management of teaching-learning processes, and measurement and evaluation. Five experts evaluated and certified this instructional model as appropriate in all aspects at a high level. 3) The results of using the instructional model found that (1) the systematic thinking skills of students who studied using the instructional model overall post-test were significantly higher than pre-test at the .05 level. (2) Measuring students’ learning achievement overall score post-test was significantly higher than pre-test at the .05 level. (3) students’ post-test scores of the experimental were significantly higher than the control group at the .05 (4) students’ post-test scores of the experimental were significantly higher than the control group at the .05.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".