Evaluation of Student Learning in Remotely Controlled Instrumental Analyses
Bibliographic record
Abstract
The Canadian Remote Sciences Laboratories (CRSL) website (www.remotelab.ca) was successfully employed in a study of the differences in the performance and perceptions of students’ about their learning in the laboratory (in-person) versus learning at a remote location (remote access). The experiment was completed both in-person and via remote access by 70 students, who performed essentially the same, academically, in the two modes. One set of students encountered the in-person laboratory first and then did the remote laboratory, while the other set of students did the activities in the reverse order. The student perception survey results (n = 46) indicated that the students found both experimental scenarios to be at appropriate levels of difficulty, clear to understand, and did not overall prefer one way of completing the experiment over the other. However, they felt that they learned more about the theory of the experiment, more hands-on skills, and more about the operation of the instrument when they performed the experiment in the laboratory in the presence of an instructor. They also believed that they learned more about the instrument operation from their laboratory partner when they completed the experiment in the laboratory, but learned more from their partner about the operation of the instrument software when they completed the procedure from a remote location.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".