First year chemistry laboratory courses for distance learners: Development and transfer credit acceptance
Bibliographic record
Abstract
In delivering chemistry courses by distance, a key challenge is to offer the learner an authentic and meaningful laboratory experience that still provides the rigour required to continue on in science. To satisfy this need, two distance general chemistry laboratory courses appropriate for Bachelor of Science (B.Sc.) students, including chemistry majors, have been recently developed at Thompson Rivers University. A constructive alignment process was employed which clearly mapped learning outcomes and activities to appropriate assessment tools. These blended laboratory courses feature custom, home experimental kits and combine elements of online and hands-on learning. The courses were designed for flexible continuous enrollment and provide online resources including tutor support, instructional videos, lab report submission, and student evaluation. The assessment of students includes laboratory reports, safety quizzes, reflective journaling, digital photo documentation, and invigilated written and online practical exams. Emphasizing the quality and rigour in these distance laboratory learning experiences allowed both courses to be accepted for B.Sc. transfer credit by other institutions, an important criterion for students. This paper will outline the design and development process of these new blended laboratory courses, their course structures and assessments, and initial student results.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 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".