University Learners’ Motivation and Experiences Using Virtual Laboratories in a Physics Course
Bibliographic record
Abstract
It is becoming necessary to examine learners’ use of and experiences with virtual laboratories. Learners’ interest and motivation to use virtual laboratories are important factors for the success of these platforms. This study was conducted to analyze Kyrgyz learners’ use of virtual laboratories in a physics course at the university level. The study was performed in the 2019–2020 spring term at a state university in Kyrgyzstan. The study took a quantitative approach, with 376 Kyrgyz learner participants studying at the undergraduate level. The participants were divided into three groups: the first and second used different virtual laboratory platforms, while the third was involved in face-to-face labs. Quantitative data were collected using an online questionnaire which consisted of items related to demographic characteristics, motivation and experience, and physics laboratory attitudes. The results demonstrated differences among the groups regarding factors of motivation and experience. In addition, learners’ physics laboratory attitudes differed with respect to gender and grade point average (GPA) factors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".