Search bioRxiv⌕ Search

bioRxiv · 10.1101/2020.11.19.390658

Supporting Student Learning and Experiences in the Lab: (How) Should We Design Their Groups?

Abstract

Undergraduate science students spend a substantial amount of time working in their laboratory groups, and instructors want to make evidence-based decisions on how to best set up these groups. Despite several studies on group composition, the evidence appears to be quite context-specific, and very little has been published about lab groups. Further, many studies focus solely on conceptual learning; however, the lab is an important venue for also supporting non-content outcomes such as confidence, process skills, team skills, and attitudes. Thus, in our introductory course on molecules, cells, and physiology we were interested in the impact of group composition, on a spread of student outcomes. Students were either placed into groups by the instructor, or self-selected into groups. To assess the impact of group composition on student outcome, we collected pre/post data from >500 students over 2 semesters. Our measures assess conceptual knowledge, confidence in lab skills, attitudes toward group learning, lab grades, gender, year of study, and (via open-ended questions) student perspectives. Using a multiple regression approach, we established models that predict student outcomes based on their individual attributes and on their lab group attributes. Surprisingly, the hetero/homogeneity of the initial group, and whether the groups were student- or instructor-selected, did not affect student outcomes in these models. Further MANCOVA analysis demonstrated that student interaction outside of the lab time was the strongest predictor of positive student attitudes toward group learning. Student perspectives on group formation are mixed, and suggest that a simple and flexible choice approach may best support our students. Overall, these findings have clear implications for our course design and instructional choices: we should focus our efforts to promote positive student interactions, rather than worrying about initial composition.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tan, T. Y., Barker, M. K.. 2020-11-20. Supporting Student Learning and Experiences in the Lab: (How) Should We Design Their Groups?. https://doi.org/10.1101/2020.11.19.390658

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗

Welfare concerns for mounted load carrying by working donkeys in Pakistan

Working donkeys (Equus asinus) are vital to peoples livelihoods. They are essential for carrying goods, however globally, overloading is one of the primary welfare concerns of working donkeys. We studied mounted load carrying by donkeys and associated factors in Pakistan. A cross-sectional study of donkey owners (n = 332) was conducted, and interviews were undertaken based on a questionnaire. Owners estimated that the median weight of their donkeys was 110kg (interquartile range (IQR) 100-120kg), and that they carried a median mounted load of 81.5kg (IQR 63-99kg). We found that 87.4% of donkeys carried a load above 50% of their bodyweight ratio (BWR), the median BWR carried was 77.1% (IQR 54.5-90.7%), and 25.3% of donkeys carried above 90% BWR. Donkeys that were loaded at more than 50% BWR were more likely to sit, compared to donkeys loaded with less weight (p=0.01). Donkeys working in peri-urban and urban areas were more likely to carry a greater BWR than donkeys working in rural areas (P<0.001), as were those carrying construction materials or bricks, compared to agricultural materials (p=0.004). Age (p=0.03) and breed (p=0.01) were also associated with carrying a higher weight. Overloading based on current recommendations (50% BWR) was common, with the majority (87.4%) of donkeys reported to carry more than the recommended 50% limit. This survey provides evidence of on-the-ground working practices and factors associated with mounted load carrying, which is critical for developing evidence-based recommendations for loading, in order to improve the welfare of working donkeys.

scientific communication and education↗