The impact of technology on student learning and staff practice in undergraduate bioscience laboratories
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».