Undergraduate Studies in Education
Bibliographic record
Abstract
ii This study sought to explore the current state of Grades 4 to 8 science education in Ontario from the perspective of Junior/Intermediate (J/I) teachers. The study’s methodology was a sequential 2-phased mixed methods explanatory design denoted as QUAN (qual) qual. Data were collected from an online survey and follow-up interviews. J/I teachers (N = 219) from 48 school boards in Ontario completed a survey that collected both quantitative and qualitative data. Interviewees were selected from the survey participant population (n = 6) to represent a range of teaching strategies, attitudes toward teaching science, and years of experience. Survey and interview questions inquired about teacher attitudes toward teaching science, academic and professional experiences, teaching strategies, support resources, and instructional time allotments. Quantitative data analyses involved the descriptive statistics and chi-square tests. Qualitative data was coded inductively and deductively. Academic background in science was found to significantly influence teachers ’ reported level of capability to teach science. The undergraduate degrees held by J/I science teachers were found to significantly influence their reported levels of capability to teach science. Participants identified a lack of time allocated for science instruction and inadequate equipment and facilities as major limitations on science instruction. Science in schools was reported to be of a “second-tiered ” value to language and mathematics. Implications of this study include improving undergraduate and preservice experiences of elementary teachers by supporting their science content knowledge and pedagogical content knowledge. iii
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 0.013 |
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".