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

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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.