Search bioRxiv⌕ Search

bioRxiv · 10.1101/2021.04.01.438071

Teacher Perceptions of Using Robots to Teach Neuroscience in Secondary School

Abstract

The use of robots to help teachers to engage students in STEM subjects has been increasing in recent years, and much progress has been made in K12 schools to incorporate robots in the pedagogy. Recent studies indicate that robots can play a significant role to engage students in neuroscience subjects, but little is known about teachers perceptions of using robots to teach neuroscience. In this paper, we present a study based on a survey questionnaire conducted with 84 teachers across multiple high schools in the United States to understand their perceptions about the usefulness of using robots to teach neuroscience. To situate teachers with an example of how robots can be used in neuroscience classrooms, we describe an educational tool called the SpikerBot. Our preliminary results indicate that there is an opportunity for neuroscience-oriented robots in secondary education, provided sufficient on-boarding and training videos.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

de Freitas, C. C. S., Hanzlick-Burton, C., Nestorovic, M., DeBoer, J., Gage, G. J., Harris, C. A.. 2021-04-02. Teacher Perceptions of Using Robots to Teach Neuroscience in Secondary School. https://doi.org/10.1101/2021.04.01.438071

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↗

Evolution of a Plastic Surgery Summer Research Program

BackgroundEarly surgical exposure and research fellowships have been independently shown to influence medical students specialty choice, increase academic productivity, and impact residency match. However, to our knowledge there is no published guidance on the implementation of formal plastic surgery summer research programs for first year medical students. We present our institutional experience developing a plastic surgery summer research program over seven years (2013-2020) in an effort to inform program development at other institutions. We hypothesized that this early, formal exposure could spark interest in pursuing research activities throughout medical school and residency. MethodsFrom 2013 to 2016, a sole basic science research arm existed. In 2017, a clinical research arm was introduced, with several supplemental activities including structured surgical skills sessions. A formalized selection process was instituted in 2014. Participant feedback was analyzed on a yearly basis. Long-term outcomes included continued research commitment, productivity, and residency match. ResultsThe applicant pool has reached 96 applicants in 2019, with 85% from outside institutions. Acceptance rate reached 7% in 2020. With adherence to a scoring rubric for applicant evaluation, good to excellent interrater reliability was achieved (ICC = 0.75). Long-term outcomes showed that on average per year, 28% of participants continued departmental research activities and 29% returned for dedicated research. Upon finishing medical school, participants had a mean of 6.9{+/-}4.0 peer-reviewed publications. 62% of participants matched into a surgical residency program, with 54% in integrated plastic surgery. ConclusionsA research program designed for first year medical students interested in plastic surgery can achieve academic goals. Students are provided with mentorship, networking opportunities, and tools for self-guided learning and career development.

scientific communication and education↗

Shedding Light on functional Near Infrared Spectroscopy and Open Science Practices

Open science practices work to increase methodological rigor, transparency, and replicability of published findings. This review aims to reflect and commemorate what the functional Near Infrared Spectroscopy (fNIRS) community has done to promote open science practices in fNIRS research and set goals to accomplish over the next ten years.

scientific communication and education↗