An official website of the United States government
Abstract Students possess informal, intuitive ways of reasoning about the world, including biological phenomena. Although useful in some cases, intuitive reasoning can also lead to the development of scientifically inaccurate ideas that conflict with central concepts taught in formal biology education settings, including evolution. Using antibiotic resistance as an example of evolution, we developed a set of reading interventions and an assessment tool to examine the extent to which differences in instructional language affect undergraduate student misconceptions and intuitive reasoning. We find that readings that confront intuitive misconceptions can be more effective in reducing those misconceptions than factual explanations of antibiotic resistance that fail to confront misconceptions. Overall, our findings build upon investigations of intuitive reasoning in biology, examine possible instructional interventions, and raise questions about effective implementation of reading interventions in addressing persistent misconceptions about biology.
more »
« less
This content will become publicly available on March 1, 2026
Spontaneous Anthropocentric Language Use in University Students’ Explanations of Biological Concepts Varies by Topic and Predicts Misconception Agreement
Previous research has shown that students employ intuitive thinking when understanding scientific concepts. Three types of intuitive thinking—essentialist, teleological, and anthropic thinking—are used in biology learning and can lead to misconceptions. However, it is unknown how commonly these types of intuitive thinking, or cognitive construals, are used spontaneously in students’ explanations across biological concepts and whether this usage is related to endorsement of construal-consistent misconceptions. In this study, we examined how frequently undergraduate students across two U.S. universities ( N = 807) used construal-consistent language (CCL) to explain in response to open-ended questions related to five core biology concepts (e.g., evolution), how CCL use differed by concept, and how this usage was related to misconceptions agreement. We found that the majority of students used some kind of CCL in the responses to these open-ended questions and that CCL use varied by target concept. We also found that students who used CCL in their response agreed more strongly with misconception statements, a relationship driven by anthropocentric language use, or language that focused on humans. These findings suggest that American university students use intuitive thinking when reasoning about biological concepts with implications for their understanding.
more »
« less
- Award ID(s):
- 2000923
- PAR ID:
- 10641979
- Editor(s):
- Fiedler, Daniela
- Publisher / Repository:
- The American Society for Cell Biology
- Date Published:
- Journal Name:
- CBE—Life Sciences Education
- Volume:
- 24
- Issue:
- 1
- ISSN:
- 1931-7913
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract BackgroundChi and colleagues have argued that some of the most challenging engineering concepts exhibit properties of emergent systems. However, students often lack a mental framework, or schema, for understanding emergence. Slotta and Chi posited that helping students develop a schema for emergent systems, referred to as schema training, would increase the understanding of challenging concepts exhibiting emergent properties. PurposeWe tested the effectiveness of schema training and explored the nature of challenging concepts from thermodynamics and heat transfer. We investigated if schema training could (a) repair misconceptions in advanced engineering students and (b) prevent them in beginning engineering students. MethodWe adapted Slotta and Chi's schema training modules and tested their impact in two studies that employed an experimental design. Items from the Thermal and Transport Concept Inventory and expert‐developed multiple‐choice questions were used to evaluate conceptual understanding of the participants. The language used by students in their open‐ended explanations of multiple‐choice questions was also coded. ResultsIn both studies, students in the experimental groups showed larger gains in their understanding of some concepts—specifically in dye diffusion and microfluidics in Study One, and in the final test for thermodynamics in Study Two. But in neither study did students exhibit any gain in conceptual questions about heat transfer. ConclusionOur studies suggest the importance of examining the nature of the phenomena underlying the concepts being taught because the language used in instruction has implications for how students understand them. Therefore, we suggest that instructors reflect on their own understanding of the concepts.more » « less
-
Abstract BackgroundTeachers often rely on the use of open‐ended questions to assess students' conceptual understanding of assigned content. Particularly in the context of mathematics; teachers use these types of questions to gain insight into the processes and strategies adopted by students in solving mathematical problems beyond what is possible through more close‐ended problem types. While these types of problems are valuable to teachers, the variation in student responses to these questions makes it difficult, and time‐consuming, to evaluate and provide directed feedback. It is a well‐studied concept that feedback, both in terms of a numeric score but more importantly in the form of teacher‐authored comments, can help guide students as to how to improve, leading to increased learning. It is for this reason that teachers need better support not only for assessing students' work but also in providing meaningful and directed feedback to students. ObjectivesIn this paper, we seek to develop, evaluate, and examine machine learning models that support automated open response assessment and feedback. MethodsWe build upon the prior research in the automatic assessment of student responses to open‐ended problems and introduce a novel approach that leverages student log data combined with machine learning and natural language processing methods. Utilizing sentence‐level semantic representations of student responses to open‐ended questions, we propose a collaborative filtering‐based approach to both predict student scores as well as recommend appropriate feedback messages for teachers to send to their students. Results and ConclusionWe find that our method outperforms previously published benchmarks across three different metrics for the task of predicting student performance. Through an error analysis, we identify several areas where future works may be able to improve upon our approach.more » « less
-
Question-asking is a crucial learning and teaching approach. It reveals different levels of students' understanding, application, and potential misconceptions. Previous studies have categorized question types into higher and lower orders, finding positive and significant associations between higher-order questions and students' critical thinking ability and their learning outcomes in different learning contexts. However, the diversity of higher-order questions, especially in collaborative learning environments. has left open the question of how they may be different from other types of dialogue that emerge from students' conversations, To address these questions, our study utilized natural language processing techniques to build a model and investigate the characteristics of students' higher-order questions. We interpreted these questions using Bloom's taxonomy, and our results reveal three types of higher-order questions during collaborative problem-solving. Students often use Why, How and What If' questions to I) understand the reason and thought process behind their partners' actions: 2) explore and analyze the project by pinpointing the problem: and 3) propose and evaluate ideas or alternative solutions. In addition. we found dialogue labeled 'Social'. 'Question - other', 'Directed at Agent', and 'Confusion/Help Seeking' shows similar underlying patterns to higher-order questions, Our findings provide insight into the different scenarios driving students' higher-order questions and inform the design of adaptive systems to deliver personalized feedback based on students' questions.more » « less
-
The theory and practice of evolutionary tree-thinking is pervasive through many scientific fields and is a critical component of biological literacy. Many elements of tree-thinking are introduced early in undergraduate biology education. However, basic concepts are often not revisited/reinforced and are assumed to have been fully conceptually grasped in upper-level courses and beyond. Here, we present a project-based activity that we developed to aid upper-level biology students to learn, conceptualize, and practice tree-thinking. This approach allows them to identify the misconceptions that they may have about tree-thinking, while reinforcing the theories and concepts that they may have encountered in introductory courses. It also integrates several pedagogical styles (instructor-led and student-centered), along with an organismal case study to make concepts concrete and realistic to students.more » « less
