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

该项目旨在开发一种动态的形成性评估方法，用于虚拟实验室。该研究的重点是如何约束虚拟实验室体验，使其能够对学生产生的相对开放的回答进行自动反馈，同时仍然为学生提供适当的探索体验。技术创新的问题是如何在有效和可靠的情况下做到这一点。该团队正专注于如何利用现有的人工智能技术，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正在为已经广泛应用于本科和高中生物课的虚拟实验室体验增加动态形成性评估能力。这是一个自动化评估项目，侧重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。对于学习者以一种不受约束的方式行动的情况，有几种方法可以实现自动评估——设计可以解释和推断自由文本的算法，或者找到设计环境的方法，使学习者可以根据深度学习的需要进行探索、开发和记录，但他们表达自己的方式更受约束，或者他们可以做的事情受到限制，但不会限制学习或参与。这个项目试图找到一个甜蜜点——一个快乐的媒介，让学习者可以探索，尝试事物，有想法，完善想法，并自由地使用想法，但要有足够的约束，让现有的人工智能算法来解释学习者在做什么，他们为什么这样做，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。人们普遍认识到，在STEM领域，必须更多地关注高阶思维技能的教学，包括实验设计、数据解释和基于证据的判断。及时的形成性评估是这种学习的一个重要组成部分，但是形成性评估是不可能由老师在最有效的时候（当学生正在从事或刚刚完成从事这种活动的时候）对整个班级的个人进行的，而且在大型高中和大学入门级课程中定期进行的劳动强度太大。因此，当学生进行基于模拟的实验并对结果进行推理时，该项目正在开发自动提供即时形成性评估的技术。调查的重点是帮助学生学会在生物学学科内进行实验和解释结果；所学到的经验教训将适用于高中和大学水平的STEM领域。

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.