UNDERSTANDING DEVELOPMENT OF SCIENTIFIC INQUIRY THROUGH LEARNER EXPECTATIONS OF AN UNDERGRADUATE PHYSICS LABORATORY PROGRAM
Bibliographic record
Abstract
BACKGROUND Education can be thought of as a system formalising cross-generational knowledge transfer, allowing learners to participate in society. ‘Folk knowledge’ acquired from their environment is added to by accumulated secondary knowledge (Geary, 2008). Science as a discipline is informed by the nature of science. Teaching scientific inquiry must adapt to the learner’s prior development (Cornish et. al., 2019). At university, the learner starts transitioning into communities of practice (Vygotsky, 1978). This is captured and measured at the Australian level by the Science Learning Threshold Learning Outcomes (Jones, Yates & Kelder, 2001; Barrie et. al., 2015). AIMS The aim of the study is to understand learner’s expectations of the development of scientific inquiry skills in the transition between learning environments. DESIGN AND METHODS A newly developed survey instrument was delivered to first-year undergraduate physics students in 2015 and 2017, receiving 1493 responses. Exploratory and confirmatory factor analysis was conducted in IBM SPSS 24 and AMOS to generate factors corresponding to scientific inquiry. RESULTS & CONCLUSIONS Regardless of their prior development, learners start university with similar expectations of developing their understanding of science inquiry. This study informs the development of students as they integrate into the community of practice of science. REFERENCES Barrie, S., Bucat, R., Buntine, M., Burke Da Silva, K., Crisp, G., George, A., Jamie, I., Kable, S., Lim, K., Pyke, S., Read, J., Sharma, M., & Yeung, A. (2015). Development, Evaluation and Use of a Student Experience Survey in Undergraduate Science Laboratories: The Advancing Science by Enhancing Learning in the Laboratory Student Laboratory Learning Experience Survey. International Journal of Science Education, 37(11), 1795–1814. https://doi.org/10.1080/09500693.2015.1052585 Cornish, S., Yeung, A., Kable, S. H., Orgill, M., & Sharma, M. D. (2019). Using teacher voices to develop the ASELL Schools professional development workshops. Teaching Science, 65(1), 4. Geary, D. (2008). An Evolutionarily Informed Education Science. Educational Psychologist: Evolution of the Educated Species, 43(4), 179–195. https://doi.org/10.1080/00461520802392133 Jones, S., Yates, B. & Kelder, J. A. (2001). Science Teaching and Learning Academic Statement. Support for the original work was provided by the Australian Learning and Teaching Council Ltd, an initiative of the Australian Government. Retrieved from http://www.acds-tlcc.edu.au/wp-content/uploads/sites/14/2016/11/altc_standards_SCIENCE_240811_v3-1.pdf. Vygotsky, L. (1978) Mind in Society: Development of Higher Psychological Processes. Cambridge MA: Harvard University Press.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".