Empirical Research: Emerging Research: Robust and Efficient Learning: Modeling and Remediating Students' Domain Knowledge

该项目采用了一个智能学习环境，称为遗传学认知导师，探索学生学习通过溯因问题解决本科遗传学课程。解决问题是STEM领域的一项重要学习活动，但有一个有据可查的风险是，学生可以根据问题情境的表面特征发展肤浅的知识，而不是更深层次的概念理解。该项目的主要假设是，浅层推理的出现是因为学生缺乏进行深层推理的概念基础，这种浅层推理可以在解决问题时发现，并在出现时立即纠正。该实证研究项目有两个主要目标，以促进遗传学问题解决中的深层推理。首先，该项目正在开发新的在线概念接地学习活动，以更好地准备学生在随后的问题解决中进行深入学习。其次，该项目采用认知建模和机器学习技术来开发一个模型，用于跟踪学生的知识，在解决问题的过程中区分真实的时间的表面和深度推理。这允许认知导师适当地干预，因为表面推理发生，通过让学生直接推理潜在的领域概念。在教授遗传学的大学的实验室研究中，正在对新的学习活动和知识追踪技术进行评估和完善，并将其推广到四所不同大学的真实课堂环境，这些大学因机构类型而异，涉及四个方面：公立与私立;国家与区域;学生的学术水平;以及种族多样性。深与浅推理的依赖措施包括反应时间，准确性和反应历史的导师，和保留，迁移和准备为未来learning.This研究目标的本科生物学教育的关键需要后测试的措施。遗传学是生物学教学的关键，既因为它是生物学的基本，统一的主题，也因为它被学生和教师视为生物学中最具挑战性的主题之一。这项研究将直接为智能学习环境的改进提供信息，例如遗传学认知导师，以促进遗传学问题解决的更深入推理。但是，浅层推理在STEM领域和其他类型的学习活动中是一个挑战。在这个项目中开发的知识应该直接导致智能解决问题的环境，支持在其他STEM领域的概念接地推理的设计准则。经验教训也可以指导研究人员在广泛的在线学习环境中创建浅推理检测器，并可以提供指导方针，在非计算机化的学习环境中排序不同类型的学习活动。

This project employs an intelligent learning environment called the Genetics Cognitive Tutor to explore student learning via abductive problem solving in undergraduate genetics courses. Problem solving is an essential learning activity across STEM domains, but there is a well-documented risk that students can develop superficial knowledge based on surface features of the problem situation, instead of a deeper conceptual understanding. The key hypotheses of this project are that shallow reasoning emerges because students lack the conceptual grounding to engage in deep reasoning, and that this shallow reasoning can be detected during problem solving and remediated as soon as it occurs.This empirical research project has two chief goals to promote deeper reasoning in genetics problem solving. First, the project is developing new on-line Conceptually Grounded Learning Activities to better prepare students for deeper learning in subsequent problem solving. Second, the project employs cognitive modeling and machine learning techniques to develop a model for tracing student knowledge that distinguishes between superficial and deep reasoning in real time during problem solving. This permits the Cognitive Tutor to intervene appropriately as superficial reasoning occurs, by engaging students in reasoning directly about underlying domain concepts. The new learning activities and knowledge tracing technique are being evaluated and refined in laboratory studies in universities where genetics is taught, and expanded to authentic classroom-based settings at four diverse universities that vary by institutional types across four dimensions: public vs. private; national vs. regional; student academic proficiency; and ethnic diversity. The dependent measures of deep vs. superficial reasoning include response time, accuracy and response history in the tutor, and post-test measures of retention, transfer, and preparation for future learning.This research targets a critical need in undergraduate biology education. Genetics is a linchpin of biology instruction, both because it is a fundamental, unifying theme of biology, and because it is viewed by students and instructors as one of the most challenging topics in biology. The research will directly inform improvements in intelligent learning environments, such as the Genetics Cognitive Tutor, to promote deeper reasoning in genetics problem solving. But shallow reasoning is a challenge across STEM domains and across other types of learning activities. The knowledge developed in this project should lead directly to design guidelines for intelligent problem-solving environments that support conceptually-grounded reasoning in other STEM domains. The lessons learned can also guide researchers in creating shallow-reasoning detectors in a broad range of on-line learning environments, and can provide guidelines for sequencing different types of learning activities in non-computerized learning environments.