COLLABORATIVE RESEARCH: Learning Progressions on the Development of Principle-based Reasoning in Undergraduate Physiology (LeaP UP)
合作研究:本科生生理学中基于原理的推理发展的学习进展(LeaP UP)
基本信息
- 批准号:1660643
- 负责人:
- 金额:$ 48.6万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-07-15 至 2022-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The complex societal problems of increasing agricultural production to meet the needs of 9 billion people by 2050, and caring for an aging population with increasingly more complex neurological and cardiovascular health issues require future scientists, physicians, and allied health professionals to develop expertise in organismal physiology. In physiology, as in other disciplines, becoming an expert in a field requires the abilities to recognize, understand and effectively reason using the principles of the discipline. During their college careers, science students often rely on rote memorization rather than principle-based reasoning to solve problems, and this leads to context-bound thinking that fails to build robust understandings. Such students can, for example, list the steps involved in muscle contraction, but cannot predict what will happen when a mutation is introduced in a muscle protein. This project will develop a "learning progression" to document how college students can develop more and more sophisticated principle-based reasoning over time to understand the physiology of animals and plants in both introductory biology and anatomy and physiology courses. Based on this learning progression, the project team will also develop open-ended assessment questions that can be scored via computer. Collectively, these tools will have the potential to transform how college students learn physiology, and to significantly enhance the quality of their resulting understanding and ability to solve related problems. The project, entitled Learning Progressions on the Development of Principle-based Reasoning in Undergraduate Physiology (LeaP UP), is supported by the Education and Human Resources Core Research Program, which funds fundamental research in STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.The LeaP UP project will develop a learning progression that describes how undergraduate students develop principle-based reasoning in the use of flux (flow down gradients) and mass balance (Conservation of Mass) in physiology. The learning progression will then guide the creation of constructed response assessments and associated computer scoring models that instructors can use to determine where their students are along the spectrum of understanding. The project team will capitalize on cutting edge advances in natural language processing and text analysis to create computer programs to accurately predict how experts would score students' responses to the conceptual constructed-response assessments. These automated scoring methods will rapidly score responses from large numbers of college students nationwide and allow the investigators to map national trends in students levels of understanding of students as they move through their undergraduate Biology and pre-Allied Health curricula at community colleges to large research universities. Thus, the tools developed will provide an organizing framework for the future redesign of undergraduate physiology curricula.
增加农业产量以满足到2050年90亿人的需求,以及照顾日益复杂的神经和心血管健康问题的老龄化人口,这些复杂的社会问题要求未来的科学家、医生和相关卫生专业人员发展生物生理学方面的专业知识。在生理学中,就像在其他学科中一样,成为某一领域的专家需要有能力识别、理解并有效地使用该学科的原理进行推理。在他们的大学生涯中,理科学生经常依靠死记硬背而不是基于原则的推理来解决问题,这导致了背景制约的思考,无法建立强有力的理解。例如,这些学生可以列出肌肉收缩所涉及的步骤,但无法预测当肌肉蛋白质中引入突变时会发生什么。这个项目将开发一个“学习进度”,以记录大学生如何随着时间的推移发展越来越复杂的基于原理的推理,以便在生物学和解剖学与生理学入门课程中理解动植物的生理学。基于这一学习进程,项目团队还将开发可通过计算机评分的开放式评估问题。总的来说,这些工具将有可能改变大学生学习生理学的方式,并显著提高他们由此产生的理解质量和解决相关问题的能力。该项目名为《本科生理学原理推理发展的学习进展》(LEAP UP),由教育和人力资源核心研究计划支持,该项目资助STEM学习和学习环境的基础研究,扩大STEM的参与,以及STEM劳动力的发展。LEAP UP项目将开发一个学习进度,描述本科生如何在生理学中使用通量(流量向下梯度)和质量平衡(质量守恒)来发展基于原理的推理。然后,学习进度将指导构建的反应评估和相关的计算机评分模型的创建,教师可以使用这些模型来确定他们的学生在理解的范围内处于什么位置。该项目团队将利用自然语言处理和文本分析方面的尖端进展来创建计算机程序,以准确预测专家将如何对学生对概念性建构反应评估的反应进行评分。这些自动评分方法将迅速获得全国大量大学生的回应,并允许调查人员绘制学生在社区学院的本科生物学和联盟前健康课程到大型研究型大学的过程中对学生的理解水平的全国趋势。因此,所开发的工具将为未来本科生理学课程的重新设计提供一个组织框架。
项目成果
期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Deconstruction of Holistic Rubrics into Analytic Rubrics for Large-Scale Assessments of Students’ Reasoning of Complex Science Concepts.
将整体评分标准解构为用于大规模学生评估的分析评分标准——复杂科学概念的推理。
- DOI:10.7275/9h7f-mp76
- 发表时间:2019
- 期刊:
- 影响因子:0
- 作者:Jescovitch, L. N.;Scott, E. E.;Cerchiara, J. A.;Doherty, J. H.;Wenderoth, M. P.;Merrill, J. E.;Urban-Lurain, M.;Haudek, K. C.
- 通讯作者:Haudek, K. C.
Covariational reasoning and item context affect language in undergraduate mass balance written explanations
- DOI:10.1152/advan.00156.2022
- 发表时间:2023-12-24
- 期刊:
- 影响因子:2.1
- 作者:Shiroda,Megan;Doherty,Jennifer H.;Haudek,Kevin C.
- 通讯作者:Haudek,Kevin C.
Ecological diversity methods improve quantitative examination of student language in short constructed responses in STEM
生态多样性方法改善了 STEM 中简短回答中学生语言的定量检查
- DOI:10.3389/feduc.2023.989836
- 发表时间:2023
- 期刊:
- 影响因子:2.3
- 作者:Shiroda, Megan;Fleming, Michael P.;Haudek, Kevin C.
- 通讯作者:Haudek, Kevin C.
Comparison of Machine Learning Performance Using Analytic and Holistic Coding Approaches Across Constructed Response Assessments Aligned to a Science Learning Progression
使用分析和整体编码方法在与科学学习进展相一致的构建响应评估中比较机器学习性能
- DOI:10.1007/s10956-020-09858-0
- 发表时间:2020
- 期刊:
- 影响因子:4.4
- 作者:Jescovitch, Lauren N.;Scott, Emily E.;Cerchiara, Jack A.;Merrill, John;Urban-Lurain, Mark;Doherty, Jennifer H.;Haudek, Kevin C.
- 通讯作者:Haudek, Kevin C.
A new assessment to monitor student performance in introductory neurophysiology: Electrochemical Gradients Assessment Device
监测学生神经生理学入门表现的新评估:电化学梯度评估装置
- DOI:10.1152/advan.00209.2018
- 发表时间:2019
- 期刊:
- 影响因子:2.1
- 作者:Cerchiara, Jack A.;Kim, Kerry J.;Meir, Eli;Wenderoth, Mary Pat;Doherty, Jennifer H.
- 通讯作者:Doherty, Jennifer H.
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Kevin Haudek其他文献
Utilizing Deep Learning AI to Analyze Scientific Models: Overcoming Challenges
- DOI:
10.1007/s10956-025-10217-0 - 发表时间:
2025-04-01 - 期刊:
- 影响因子:5.500
- 作者:
Tingting Li;Kevin Haudek;Joseph Krajcik - 通讯作者:
Joseph Krajcik
Employing automatic analysis tools aligned to learning progressions to assess knowledge application and support learning in STEM
- DOI:
10.1186/s40594-024-00516-0 - 发表时间:
2024-11-08 - 期刊:
- 影响因子:8.000
- 作者:
Leonora Kaldaras;Kevin Haudek;Joseph Krajcik - 通讯作者:
Joseph Krajcik
Kevin Haudek的其他文献
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{{ truncateString('Kevin Haudek', 18)}}的其他基金
Developing Open Response Assessments to Evaluate How Undergraduates Engage in Mathematical Sensemaking in Biology, Chemistry, and Physics
开发开放式反应评估来评估本科生如何参与生物学、化学和物理领域的数学意义建构
- 批准号:
2235487 - 财政年份:2023
- 资助金额:
$ 48.6万 - 项目类别:
Standard Grant
Evaluating Effects of Automatic Feedback Aligned to a Learning Progression to Promote Knowledge-In-Use
评估与学习进度相一致的自动反馈对促进知识使用的效果
- 批准号:
2200757 - 财政年份:2022
- 资助金额:
$ 48.6万 - 项目类别:
Continuing Grant
Developing a Next Generation Concept Inventory to Help Environmental Programs Evaluate Student Knowledge of Complex Food-Energy-Water Systems
开发下一代概念清单,以帮助环境项目评估学生对复杂食物-能源-水系统的了解
- 批准号:
2013359 - 财政年份:2020
- 资助金额:
$ 48.6万 - 项目类别:
Standard Grant
Collaborative Research: ArguLex - Applying Automated Analysis to a Learning Progression for Argumentation
协作研究:ArguLex - 将自动分析应用于论证的学习进程
- 批准号:
1561159 - 财政年份:2016
- 资助金额:
$ 48.6万 - 项目类别:
Standard Grant
Collaborative Research: PCK*Lex: Applying Computerized Lexical Analysis to Develop a Cost-Effective Measure of Science Teacher Pedagogical Content Knowledge
合作研究:PCK*Lex:应用计算机词汇分析来开发科学教师教学内容知识的经济有效的衡量标准
- 批准号:
1438739 - 财政年份:2014
- 资助金额:
$ 48.6万 - 项目类别:
Standard Grant
Collaborative Research: Expanding a National Network for Automated Analysis of Constructed Response Assessments to Reveal Student Thinking in STEM
合作研究:扩大构建反应评估自动分析的国家网络,以揭示学生在 STEM 中的思维
- 批准号:
1323162 - 财政年份:2013
- 资助金额:
$ 48.6万 - 项目类别:
Continuing Grant
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