A Review to Weigh the Pros and Cons of Online, Remote, and Distance Science Laboratory Experiences
Bibliographic record
Abstract
The effectiveness of traditional face to face labs versus non-traditional online, remote, or distance labs is difficult to assess due to the lack of continuity in the literature between terminology, standard evaluation metrics, and the use of a wide variety non-traditional laboratory experience for online courses. This narrative review presents a representative view of the existing literature in order to identify the strengths and weaknesses of non-traditional laboratories and to highlight the areas of opportunity for research.Non-traditional labs are increasingly utilized in higher education. The research indicates that these non-traditional approaches to a science laboratory experience are as effective at achieving the learning outcomes as traditional labs. While this is an important parameter, this review outlines further important considerations such as operating and maintenance cost, growth potential, and safety. This comparison identifies several weaknesses in the existing literature. While it is clear that traditional labs aid in the development of practical and procedural skills, there is a lack of research exploring if non-traditional laboratory experiments hinder student success in subsequent traditional labs. Additionally, remote lab kits blur the lines between modality by bringing experiences that are more tactile to students outside of the traditional laboratory environment. Though novel work on non-traditional labs continues to be published, investigations are still needed regarding cost differences, acquisition of procedural skills, preparation for advanced work, and instructor contact time between traditional and non-traditional laboratories.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".