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

本项目采用名为遗传学认知导师的智能学习环境，探索学生在本科遗传学课程中通过溯因性问题解决的学习方式。解决问题是STEM领域的一项基本学习活动，但有充分证据表明，学生可能会根据问题情况的表面特征发展肤浅的知识，而不是更深入的概念理解。这个项目的关键假设是，浅层推理之所以出现，是因为学生缺乏参与深层推理的概念基础，而且这种浅层推理可以在解决问题的过程中被发现，并在问题发生时立即纠正。这个实证研究项目有两个主要目标，即促进遗传学问题解决的更深层次推理。首先，该项目正在开发新的在线概念基础学习活动，以便为学生在随后的问题解决中进行更深入的学习做好准备。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实时区分表层和深层推理。这允许认知导师在表面推理发生时进行适当的干预，通过让学生直接对潜在的领域概念进行推理。新的学习活动和知识追踪技术正在遗传学教授的大学的实验室研究中进行评估和完善，并扩展到四所不同大学的真实课堂环境中，这些大学因机构类型在四个方面有所不同：公立与私立；国家vs.地区；学生学术水平；还有种族多样性。深度推理与肤浅推理的相关测量包括导师的反应时间、准确性和反应历史，以及测试后的保留、转移和对未来学习的准备测量。本研究的目标是本科生物学教育的关键需求。遗传学是生物学教学的关键，因为它是生物学的一个基本的、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中的更深层次推理。但浅层推理在STEM领域和其他类型的学习活动中都是一个挑战。在这个项目中开发的知识应该直接导致智能问题解决环境的设计指南，支持其他STEM领域的概念基础推理。所获得的经验教训还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，并可以为在非计算机化学习环境中排序不同类型的学习活动提供指导。

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.