STUDENT EXPERIENCES IN LABORATORY PROGRAMS ACROSS THREE UNIVERSITIES: A SNAPSHOT DURING COVID-19 SEMESTER
Bibliographic record
Abstract
This project focuses on the novel idea of integration of technologies with inquiry skills and modelling (Crook & Sharma, 2013; Gilbert, 2004; Gordon et al., 2019) and associates these with students’ cognitive engagement, behavioural engagement and emotional engagement (Muller, Sharma & Reimann, 2008; Kota, Cornish & Sharma, 2019; Cornish et al., 2019). We use a survey (Barrie et al., 2015) that measures student experiences in laboratories: how technology was integrated, how much inquiry skills are developed, and how well the students understand the modelling. While the survey is designed for hands-on labs, the emergence of COVID-19 pandemic at the start of the 2020 semester has necessitated for undergraduate lab programs to shift to online delivery mode. This has created an interesting opportunity to use this survey for measuring the impact of online labs on student engagement, and we have obtained modified ethics approval for that. The data is being collected in the final weeks of semester 1 from the first-year undergraduate students enrolled in physics units at the University of Melbourne, the University of Sydney and Monash University. Two types of responses to the engagement in labs will be explored: (1) from students who experienced some face to face labs at the start, then moved to online labs; and (2) from students who only had online labs. It would be interesting to see which aspects of the laboratory practice have retained their original learning intention, and which aspects have been affected by the online delivery, and to what extent. REFERENCES Barrie, S. C., Bucat, R. B., Buntine, M. A., Burke da Silva, K., Crisp, G. T., George, A. V., & 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. 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. Crook, S. J. & Sharma, M. D. (2013). Bloom-ing heck! The activities of Australian science teachers and students two years into a 1:1 laptop program across 14 high schools. International Journal of Innovation in Science and Mathematics Education, 21(1), 54-69. Gilbert, J. K. (2004). Models and modelling: Routes to more authentic science education. International Journal of Science and Mathematics Education, 2(2), 115–130. Gordon, T., Georgiou, H., Cornish, S., & Sharma, M. (2019). Science in your pocket: Leaving high school students to their own 'devices' while designing an inquiry-based investigation. Teaching Science, 65(1), 17. Kota, S. D., Cornish, S, & Sharma, M. D. (2019); Switched on! Student and teacher engagement in an electricity practical, Physics Education, 54(1), 1-9. Muller, D. A., Sharma, M. D., & Reimann P 2008 Raising cognitive load with linear multimedia to promote conceptual change, Science Education, 92, 278–296.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".