NSF Award
1418219
This Research on Education and Learning (REAL) project arises from an October 2014 Ideas Lab on Data-intensive Research to Improve Teaching and Learning. The intentions of that effort were to: (1) bring together researchers from across disciplines to foster novel, transformative, multidisciplinary approaches to using the data in large education-related data sets to create actionable knowledge for improving STEM teaching and learning environments in the medium term; and (2) revolutionize learning in the longer term. In this project, researchers from the Educational Testing Service, Columbia University Teachers' College, Arizona State University, and North Carolina State University will conduct data-driven, exploratory analyses to identify key places where social interactions impact learning outcomes in specific learning environments, with the goal of improving teaching and learning in large-scale STEM courses.<br/><br/>This research takes advantage of data traces left in large-scale blended and online learning environments (including massively open online courses, or MOOCs). The researchers will develop a comprehensive model for social learning in the context of such courses that will enable assessment of both the collaborative needs of individuals within the context of a class, and the quality of collaborations they are carrying out. Such diagnoses will allow both instructors and automated systems to provide advice to learners about the peers they might work with to enhance their learning (e.g., regarding the kinds of social interactions that will foster better understanding and development of important disciplinary capabilities). An interdisciplinary team of investigators with expertise in theory-driven educational data mining, natural-language processing, psychometrics, social-network analysis, and computer support for collaborative learning will collaborate to explore when learners in blended and online classes benefit from social interactions, and to understand how to identify more and less productive collaborative interactions. The researchers will use data from three blended and online classes (e.g., log files capturing collaborative discussions, individual and collaborative interactions around well-instrumented examples, peer tutoring sessions, pair programming labs, paired projects) and a variety of data analysis approaches (e.g., text analysis, machine learning) to determine: (1) which cognitive, social, and affective dimensions of need and interaction can be identified from available data; (2) which analyses are useful in providing action-oriented collaboration advice; and (3) what additional types of data may be needed for making such recommendations. This exploration will be grounded in theories of social interactions for learning (e.g., self-explanation, dialectic with oneself and others, zone of proximal development, social learning theory of Bandura, peripheral and centripetal participation).
