Exploring High School Students’ Cognitive Structures for Energy Concept Through Word Association Test
Bibliographic record
Abstract
This research, which was conducted in order to reveal the cognitive structures of high school students regarding the concept of energy through the word association test, was designed in a survey model. Word association test was used as a data collection tool. After the necessary information was given to the students, they were given three minutes and they were asked to write the first words that came to mind about the key concept. The study group of the research consists of 202 students studying in a high school in the province of Malatya in the 2019-2020 academic year. 58 of these students are 9th grade, 46 are 10th grade, 45 are 11th grade and 53 are 12th grade. While choosing the study group of the research, one of the purposeful sampling types was chosen based on easily accessible situation sampling. The words obtained related to the concept of energy are tabulated using Microsoft office excel program. The cut-off points of the tables, which were examined in detail and repeated words were taken into account, were determined. Concept networks were created in line with the determined cut-off points. In the research, 189 different words were produced for the concept of energy by high school students. While 114 of these words were not included in the analysis because they had 4 or less frequencies, 75 words were included in the analysis. Among the words included in the analysis, the most repeated word in the 9th grade was “petrol” (f = 30), the most repeated word in the 10th grade was “fossil fuel” (24), the most repeated word in the 11th grade “electricity” (19), the most repeated word in the 12th grade was found to be “potential” (28). In the comparison between the classes, a difference in quantity and quality is observed. It has been observed that as the grade level increases, the quality of the answers given increases.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".