DIP: Using dynamic formative assessment models to enhance learning of the experimental process in biology

DIP：使用动态形成性评估模型来加强对生物学实验过程的学习

• 批准号: 1227245
• 负责人: Eli Meir
• 金额: $ 133.34万
• 依托单位: SimBiotic Software
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1227245&HistoricalAwards=false
• 关键词: DIP; Using; dynamic; formative; assessment

This project seeks to develop a dynamic formative assessment method for use with virtual labs. The research focuses on how to constrain a virtual lab experience to be amenable to automated feedback on relatively open-ended responses students are generating while still giving students an appropriate exploratory experience. The technology innovation question is how to do that with validity and reliability. The team is focusing on how to use available artificial intelligence technologies to make it possible to provide good feedback, both to learners working in these environments and to their teachers. PIs are adding dynamic formative assessment capabilities to virtual lab experiences that are already extensively used in undergraduate and high-school biology classes.This is an automated assessment project, focusing on assessing learner understanding and capabilities in situations where learners are exploring, having, and using ideas as they are learning STEM content and practices. There are several ways one could approach automated assessment for situations where learners are acting in a fairly unconstrained way -- design algorithms that can interpret and make inferences from free text, or find ways to design the environment in such a way that learners can explore, develop, and record as needed for deep learning but where they have a more constrained way of expressing themselves or limitations in what they can do that don't constrain the learning or engagement. This project seeks to find a sweet spot -- a happy medium where learners can explore, try things out, have ideas, refine ideas, and use ideas with significant freedom but just enough constraint for already-existing artificial intelligence algorithms to interpret what learners are doing, why they are doing it, and what they mean to express. Learning how to do this is essential to designing the learning environments of the future.There is broad acknowledgement that more attention must be given in STEM fields to the teaching of higher-order thinking skills, including experimental design, data interpretation and evidence-based judgment. Timely formative assessment is a crucial component of such learning, but formative assessment is impossible for a teacher to do for a whole class of individuals at the time when it will have the most effect (when students are engaging in or have just finished engaging in such activities) and too labor intensive to be done regularly in large high school and introductory-level college classes. This project is therefore developing techniques for automatically providing immediate formative assessment as students are conducting simulation-based experiments and reasoning about their results. The investigation focuses on helping students learn to conduct experiments and interpret results within the discipline of biology; lessons learned will be applicable across STEM domains at the high-school and college levels.

本项目旨在开发一种用于虚拟实验室的动态形成性评估方法。该研究的重点是如何限制虚拟实验室的经验，以适应自动反馈的相对开放式的反应，学生正在生成，同时仍然给学生一个适当的探索性的经验。技术创新的问题是如何做到有效性和可靠性。该团队专注于如何使用现有的人工智能技术，以便为在这些环境中工作的学习者和他们的老师提供良好的反馈。PI正在将动态形成性评估功能添加到已经广泛用于本科和高中生物课程的虚拟实验室体验中，这是一个自动化评估项目，专注于评估学习者在学习STEM内容和实践时探索，拥有和使用想法的情况下的理解和能力。对于学习者以相当不受约束的方式行事的情况，有几种方法可以实现自动化评估-设计可以解释并从自由文本中进行推断的算法，或者找到设计环境的方法，使学习者可以探索，发展，并根据深度学习的需要进行记录，但他们表达自己的方式更受限制，或者他们可以做的事情受到限制，学习或参与。该项目旨在找到一个最佳点-一个快乐的媒介，学习者可以探索，尝试事物，有想法，提炼想法，并以显着的自由使用想法，但对现有的人工智能算法有足够的约束来解释学习者正在做什么，为什么他们这样做，以及他们想表达什么。学习如何做到这一点对于设计未来的学习环境至关重要。人们普遍认为，STEM领域必须更加关注高阶思维技能的教学，包括实验设计、数据解释和循证判断。及时的形成性评估是这种学习的重要组成部分，但是形成性评估对于教师来说是不可能的，因为它在最有效的时候（当学生正在从事或刚刚完成这些活动时）对整个班级的个人进行评估，并且在大型高中和入门级大学班级中定期进行过于劳动密集。因此，该项目正在开发自动提供即时形成性评估的技术，因为学生正在进行基于模拟的实验和对结果的推理。调查的重点是帮助学生学习进行实验，并解释生物学学科的结果;学到的经验教训将适用于高中和大学层面的STEM领域。