A Systematic Literature Review on Trends in the Use of Science Experiments in Online Learning Environments
Bibliographic record
Abstract
Experiments are considered to be essential components of science learning. This research aimed to investigate trends in the use of science experiments in online learning. A systematic literature review was carried out, with data sourced from the Scopus and Google Scholar databases. The reviewed documents were journal articles published between 2015 and 2022, with the keywords “science practicum,” “science experiments,” “distance learning,” “online learning,” and “hands-on science.” Using Harzing’s Publish or Perish software, 970 articles were found but only 32 were reviewed. The literature review followed a procedure adapted from the preferred reporting items for systematic reviews and meta-analyses (PRISMA), with articles reviewed based on predetermined criteria such as the year of publication, article source, practicum topics, research subjects, assessment methods, technology, and experiment design in online learning. In the results, various designs for online learning models, the technology used in science experiments, topics addressed, and appropriate assessment methods were identified. Trends included the extensive use of interactive simulation models in online science experiments, the use of virtual laboratories as a crucial technology, and the use of experiment reports to assess students. The analysis showed a sharp increase in the number of publications since the pandemic (2020) and that online science experiments might be carried out effectively by considering the characteristics of the material, matching the science curriculum, and using assessments that fulfill the objectives of science experiments.
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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 | 0.041 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.030 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".