The Influence of The Problem-Based Learning with Radical Constructivism Module on Students' Problem-Solving Skills
Bibliographic record
Abstract
Problem solving skills (PSS) are one of the high-level thinking skills that must be possessed by students. This skill will help students apply scientific content to solve problems in real life. The purpose of this study was to determine the effect of problem based learning (PBL) models with a radical construktivism module on PSS. Data were collected in February to March 2022. The method in this study used an experimental method with nonrandomized post test only control-group design. This research was conducted in two classes: the experimental class using PBL model learning with radical constructivism module and the control class that only using PBL model learning. The samples were 160 students. The instrument was tests to assess PSS. Data analysis used independent sample t-test to determine effect of PBL model learning with radical constructivism module on PSS. T-test results showed that value sig 0,05. The result showed that students who received PBL model learning and the radical constructivism module obtained significantly better PSS compared to students who only received PBL model learning
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 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".