STEM Learning and Learning Environments 
Broadening Participation in STEM
Using Data Mining and Observation to derive an enhanced theory of SRL in Science learning environments
Effective Years: 2016-2021
This project aims to enhance theory and measurement of students' self-regulated learning (SRL) processes during science learning. SRL refers to learning that is guided by metacognition (thinking about one's thinking), strategic action (planning, monitoring, and evaluating personal progress against a standard), and motivation to learn. The project will accomplish this by developing a technology-based framework which leverages human expert judgment and machine learning methods to identify key moments during SRL and analyze these moments in depth. The project uses an existing science learning environment, Betty's Brain, that combines learning-by-modeling with critical thinking and problem solving skills to teach complex science topics. The environment is designed to have the student teach Betty science topics using concept maps (the critical elements of the science) and reading materials provided by the environment. A critical component of this project is to determine when a student using the system needs help. Using SRL as a basis, the additions to Betty's brain will identify key points in the SRL processes of metacognition, strategic action, and motivation. Some of these points can be determined automatically in recognizing key points while others require human intervention to recognize key points and then to determine what actions should be taken to enable the student. This information will be recorded and the experiences then are used to update the automatic identification of key points.
The project's main intellectual merit is in integrating the power of data mining to rapidly sift through large amounts of data to find key inflection or change points in student reasoning and strategies, with the power of human beings to deeply understand other humans' SRL processes. This measurement framework, with the accompanying detectors of inflection points in students' SRL in online learning, has the potential to transform science learning and teaching in K-12 settings by providing insights into how SRL unfolds during learning through the interactions between affect, engagement, cognition and metacognition. Those insights will be used to extend an existing theory of SRL, increasing its richness, specificity, and predictive power. Self-regulated learning is important to student success, both in K-12 education and during life-long learning afterwards. Better understanding of SRL processes will support the development of computer-based science learning environments, such as Betty's Brain, with the capability to better support students' learning of SRL skills and strategies in science classrooms. By studying these issues within the diverse population of urban students who currently use Betty's Brain, success of the project will increase the relevance of SRL to the full diversity of America's learners. The project's software will be available through the portal www.teachableagents.org.
