The Role of Technology in Undergraduate Bioscience Laboratory Learning: Bridging the Gap between Theory and Practice
Bibliographic record
Abstract
Integration of technology is widespread in laboratory teaching, whose purpose includes building theoretical understanding and practical skills. How second-year bioscience undergraduate students at a UK university use technology to construct their understanding of laboratory-based topics was investigated using a concurrent think-aloud protocol in the laboratory, followed by semi-structured interviews. Analysis of think-aloud data used socially shared metacognitive coding since students may co-construct their understanding in these collaborative spaces. This analysis demonstrated that participants used technology within the laboratory either as a tool to conduct their experiment or, as a source of information to help them understand, apply or perform their experimental task. Semi-structured interviews demonstrated that students integrated technology into all aspects of their laboratory learning. Eight out of the ten participants described using technology to help them make connections between theory and practice as part of post-laboratory activities such as analysing or conducting further research on the topic. A survey of UK bioscience undergraduate modules found that 22% of modules did not use post-laboratory activities, suggesting that more scaffolding of post-laboratory activities could provide bioscience students with greater integration of practical and theoretical understanding and consequently meaningful laboratory learning.
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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 | 0.033 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".