The impact of technology on student learning and staff practice in undergraduate bioscience laboratories
Bibliographic record
Abstract
The bioscience laboratory is a complex learning environment with a high cognitive load resulting from unfamiliar processes and equipment, which can make learning challenging. With the increasing use of technology in education, this study uses a mixed methods approach to examine the impact of technology on learning in this environment through the case study of a large multi-purpose “Superlab” at Nottingham Trent University, as well as examining the use of pre- and post-laboratory activities to support laboratory learning across UK HE institutions in biosciences. \n \nUse of a concurrent think aloud approach in laboratory classes demonstrated that undergraduate bioscience students used technology to undertake experiments and access information. These students perceived the laboratory as an environment for developing their skills, with changes in theoretical understanding occurring as a result of post-laboratory activities such as report writing or reflective practice. Only two thirds of UK HE bioscience modules surveyed stated that they used post-laboratory activities, suggesting a missed opportunity in some cases for scaffolding consolidation of student learning. \n \nData from the semi-structured interviews and the digital history survey confirmed that student participants were comfortable with range of technologies that were integrated into both their everyday life and learning. Comparison of these skills against preliminary data from bioscience graduate employers further suggested that by the time they graduated, a high proportion of bioscience students had the key technology-based skills that they required. \n \nDespite this, anxiety or caution around using laboratory equipment was frequently expressed based on its cost or the unfamiliarity of the equipment, or the implications of errors on assessed practical classes. The survey data from UK HE institutions highlighted that one-third of bioscience modules do not use pre-laboratory activities, thereby missing an opportunity to reduce student anxiety and cognitive load by familiarising students with equipment, potentially facilitating greater lab learning. \n \nThese findings are particularly pertinent given the impact of the COVID-19 in diversifying laboratory education, and the additional pre- and post-laboratory support needed for students whose access to laboratories has been limited by the pandemic.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".