Concept Cartoons Supported Problem Based Learning Method in Middle School Science Classrooms
Bibliographic record
Abstract
Problem based learning, in which events from daily life are presented as interesting scenarios, is one of the active learning approaches that encourages students to self-direct learning. Problem based learning, generally used in higher education, requires students to use high end thinking skills in learning environments. In order to use effectively for secondary students, concept cartoons can be integrated to problem based learning environment. Concept cartoons provide alternative views and help students at problem solving stage. So, in this study the effects of concept cartoons and problem based learning on students’ inquiry learning skill perceptions and levels of relating knowledge with daily life were examined. Two experiment groups and one control group were formed and pre test-post test control group quasi experimental design was used in this research. During four weeks experimental practice, courses were continued with concept cartoon integrated problem based learning in experiment group 1 while only problem based learning was used in experiment group 2 and science program activities was used in control group. 553 sixth grade students (13-14 age group) from 9 different middle schools in Turkey participated in this study. Inquiry learning skills perception scale and knowledge-daily life relating open ended questions were used as data collection tools. In the result of the data anaylsis, it was found that students’ inquiry learning skill perceptions scores differ significantly in favor of experiment groups. However, no significant difference found between groups in terms of knowledge-daily life relation scores.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".