bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.02.28.640796

Sketchy understandings: Drawings reveal where students may need additional support to understand scale and abstraction in common representations of DNA

Abstract

Visual representations in molecular biology tend to follow a set of shared conventions for using certain shapes and symbols to convey information about the size and structure of nucleotides, genes, and chromosomes. Understanding how and why biologists use these conventions to represent DNA is a key part of visual literacy in molecular biology. Visual literacy, which is the ability to read and interpret visual representations, encompasses a set of skills that are necessary for biologists to effectively use models to communicate about molecular structures that cannot be directly observed. To gauge students visual literacy skills, we conducted semi-structured interviews with undergraduate students who had completed at least a year of biology courses. We asked students to draw and interpret figures of nucleotides, genes, and chromosomes, and we analyzed their drawings for adherence to conventions for representing scale and abstraction. We found that 77% of students made errors in representing scale and 86% of students made errors in representing abstraction. We also observed about half of the students in our sample using the conventional shapes and symbols to represent DNA in unconventional ways. These unconventional sketches may signal an incomplete understanding of the structure and function of DNA. Our findings indicate that students may need additional instructional support to interpret the conventions in common representations of DNA. We highlight opportunities for instructors to scaffold visual literacy skills into their teaching to help students better understand visual conventions for representing scale and abstraction in molecular biology.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Uminski, C., Wright, L. K., Newman, D. L.. 2025-03-02. Sketchy understandings: Drawings reveal where students may need additional support to understand scale and abstraction in common representations of DNA. https://doi.org/10.1101/2025.02.28.640796

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↗

A strong start for sustained success: inclusivity through a national group mentorship program for first-year graduate students

In the United States, STEM graduate programs and workforce do not represent the demographics of the population. Obstacles, including a lack of transparency, community, and accessible information in navigating academia, disproportionately affect students from underserved backgrounds. Peer mentoring networks can address these disparities. Here, we describe Cientifico Latino, Inc.s Graduate Student Engagement and Community (CL-GSEC) program, a nationwide, group-based peer mentorship program that has served first-year graduate students across the U.S., especially those from underserved backgrounds. Surveys indicate CL-GSEC positively impacts the first-year graduate experience. We highlight key program features, challenges, and insights, such as financial strains faced by first-year graduate students. We offer suggestions for how faculty and departments can better support students during this critical early stage of graduate training. We hope that reporting on CL-GSECs program structure, evaluations, and findings will guide educational leaders in expanding programming for junior graduate students.

scientific communication and education↗

Biodesign Buddy: Integrating Generative Artificial Intelligence in Academic Biodesign

Biodesign is an interdisciplinary research domain that incorporates principles from design and the life sciences to develop new systems, processes, and objects. Collegiate biodesign educators face unique pedagogical challenges, including an absence of relevant scholarship on curriculum design and instructional best practices for cultivating student scientific literacy. These difficulties may be overcome with newly available technologies, like generative AI systems, that enable personalized learning through domain-specific semantic spaces. This article examines the instructional value of one such domain-specific LLM, Biodesign Buddy, through a mixed-methods analysis of an eight-week study involving 64 students participating in an international biodesign competition. Results indicate strong support for integrating AI into biodesign coursework. Surveys captured attitudes toward AI, scientific literature, and learning experiences to assess AIs impact on learning outcomes. Findings suggest that integrating AI into biodesign pedagogy can meaningfully redress conceptual issues in biodesign while informing broader debates on AIs role in higher education. Impact StatementThis article introduces Biodesign Buddy, a domain-specific generative AI system for collegiate biodesign education, and reports on its exploratory deployment, offering design principles and preliminary findings to inform the development of AI-supported pedagogies for interdisciplinary biodesign instruction.

scientific communication and education↗