Understanding of the Nature of Science: A Comparative Study of Canadian and Korean Students
Bibliographic record
Abstract
This study was designed to identify students’ perceptions of learning activities, assessment formats, and content on their understanding of the nature of science (NOS) by comparing and examining constructs created by Canadian and Korean students. Participants were 217 Canadian and 319 Korean Grade 8 students that filled out questionnaires; additionally, 9 students volunteered for semi-structured interviews. Descriptive statistics, multivariate analysis of variance and partial least squares were used to examine the quantitative data. A conceptually clustered matrix was used for the qualitative analyses. Results indicated that students from both countries perceived 1) their learning activities were teacher-directed, 2) class presentations and discussions occurred least frequently, 3) paper-and-pencil tests determined science scores, 4) science tests relied heavily on knowledge of science while knowledge about science was least likely to be assessed, and 5) generally students held relativistic views on science. The effect for country on NOS concepts was statistically significant across all of their perceptions except for the concepts of culturally embedded science and the perceptions of short-answer test formats. Specifically, Canadian students perceived that they had relatively more student-directed activities while Korean students perceived that they had more teacher-directed science lab activities. Further, Canadian students were inclined to hold more relativistic views across the NOS concepts. It was also noted that Korean students provided more political examples while Canadian students provided stem cell research or environmental issues. An examination of associations revealed that students’ learning activities, assessment formats, and content are good predictors of NOS understanding since these constructs explain variances from 19.7% for Empirical NOS to 63% for Scientific Methods. Results from students’ open-ended responses to the NOS concepts and the semi-structured interviews were consistent with the quantitative analyses. Most interviewees agreed that what, and how, they learned science-- and how their learning was assessed--affected their views of science since school science education was the important factor in developing their scientific knowledge. These results imply that diverse learning activities and assessments could prove to be a better approach to enhancing students’ understanding of NOS than teacher-directed learning activities and test formats requiring a single correct answer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".