Knowing What Students Know: Using Educational Data Mining to Predict Robust STEM Learning

学生对科学，技术，工程和数学（STEM）概念的理解经常受到学生将浅层问题解决策略应用于STEM内容领域的影响。 浅层问题解决技能是指适用于问题的特定实例，但不适用于类似问题的技能。 这个研究项目将研究如何在基于计算机的学习环境中支持对STEM概念的深入理解-特别是遗传学问题解决。 学习环境将支持遗传学过程建模理解和遗传学溯因推理-即从经验数据推理到可能生成该数据的遗传过程。 过程建模和溯因推理模型都与许多STEM领域相关。开发支持基因组分析学习的基于计算机的活动，并了解学生对该主题的强大知识，有望通过改善STEM学习产生巨大的社会效益。本研究将联合收割机认知建模和教育数据挖掘相结合，开发和评估学生在问题解决过程中学习深度的多成分模型。 拟议的研究解决了学生解决问题的一个有据可查的缺点，在STEM领域中观察到：一些学生发展了浅薄的解决问题的知识，他们很难在新的情况下应用。 拟议的研究将开发一个多组件，但简约，学生的知识深度模型，预测在知识的直接转移，并在未来的学习，在解决问题的课程连续的主题的个体差异。这项研究将发生在现代遗传学解决问题的领域。遗传学是生物学的一个基本的，统一的主题，被学生和教师视为生物学中最具挑战性的主题之一，部分原因是它在很大程度上依赖于解决问题。遗传学问题的解决依赖于两种类型的知识：遗传过程建模知识和溯因推理技能-从经验数据向后推理生成数据的遗传过程。拟议的研究将侧重于学生对基因组通路分析的理解，这是一个连接经典孟德尔遗传学和分子遗传学的基础性课题，是理解21世纪生物学进展的关键。本计画将建构学生遗传学知识的四个组成部分：（1）基因作用的语言陈述知识;（2）遗传学过程建构知识;（3）溯因推理技能;（4）元认知技能。本研究将探讨如何将这些措施整合到一个统一的模型中，预测未来学习过程中的个体差异;也就是说，学生知识的哪些组成部分会影响截取（直接迁移），哪些会影响学生在整个课程中连续主题的学习功能的速度。该项目将采用遗传学认知导师，智能学习环境，为学生提供解决问题所需的一步一步的支持。该项目利用现有的解决问题的模块，将建立一些额外的模块，并将使所有模块的附带教学文本上线，将学生阅读时间的措施纳入一个强大的学习模式。最近的学生建模工作已经取得了初步的，有希望的结果，奠定了一个完整的模型的基础：（1）模型，推断学生的知识，从一步一步的准确性，在基本的问题解决可靠地预测两个基本的问题解决后测表现，和鲁棒的学习后测措施（2）学生的元认知技能模型更准确地预测了这些稳健的学习措施;（3）自动性模型准确地预测了“浅”学习。该项目将把这些组成部分和陈述性知识措施整合到一个综合框架中，该框架模拟学生在解决问题方面的稳健学习，不仅预测学生在稳健学习测试中的表现时间片，而且预测学生完成连续解决问题课程单元时整个学习功能的个体差异。

Student understanding of Science, Technology, Engineering and Mathematics (STEM) concepts frequently suffers from students applying shallow problem solving strategies to STEM content areas. Shallow problem solving skills are skills that apply to a specific instance of a problem but do not generalize to similar problems. This research project will study how deep understanding of STEM concepts can be supported in a computer based learning environment - specifically genetics problem solving. The learning environment will support both genetics process-modeling understanding and genetics abductive reasoning - that is reasoning from empirical data to the genetic processes that could generate that data. Both process modeling and abductive reasoning models are relevant to many STEM domains. Developing computer-based activities that support learning of genomic analysis, and understanding robust student knowledge of this topic promise to yield large societal benefits by improving STEM learning.The research will combine cognitive modeling and educational data mining to develop and evaluate a multi-component model of the depth of student learning during problem solving. The proposed research addresses a well-documented shortcoming in student problem solving, observed across STEM domains: some students develop shallow problem-solving knowledge that they have difficulty applying in new situations. The proposed research will develop a multi-component, yet parsimonious, model of students' depth of knowledge that predicts individual differences in direct transfer of knowledge to, and future learning of, successive topics in a problem-solving curriculum. This research will occur in the domain of modern genetics problem solving. Genetics is a fundamental, unifying theme of biology and is viewed as one of the most challenging topics in biology by students and instructors, in part because it relies heavily on problem solving. Genetics problem solving relies on two types of knowledge: genetic process-modeling knowledge, and abductive reasoning skills - reasoning backwards from empirical data to the genetic processes that generated the data. The proposed research will focus on student understanding of genomic pathway analysis, a foundational topic linking classic Mendelian genetics and molecular genetics that is key to understanding advances in 21st century biology. This project will model 4 components of students' genetics knowledge: (1) verbal declarative knowledge of gene action; (2) genetics process modeling knowledge; (3) abductive reasoning skill; and (4) metacognitive skills. The research will examine hypotheses about how to integrate these measures into a unified model that predicts individual differences in future learning processes; that is, which components of student knowledge affect the intercept (direct transfer), and which affect the rate, of students' learning functions in successive topics across the curriculum. The project will employ the Genetics Cognitive Tutor, an intelligent learning environment that provides students step-by-step support as needed in solving problems. The project leverages existing problem-solving modules, will build some additional modules, and will bring the accompanying instructional text for all modules on-line to incorporate student reading-time measures into a robust learning model. Recent student modeling efforts have yielded preliminary, promising results that lay the foundation for a complete model: (1) models that infer students' knowledge from step-by-step accuracy in basic problem-solving reliably predict both basic problem-solving posttest performance, and robust learning posttest measures (transfer and preparation for future learning; (2) models of students' metacognitive skills more accurately predict these robust learning measures; and (3) models of automaticity accurately predict "shallow" learning. This project will integrate these components and declarative knowledge measures into a comprehensive framework that models students' robust learning in problem-solving and predicts not only time-slices of student performance on robust-learning tests, but predicts individual differences in entire learning functions as students complete successive problem-solving curriculum units.