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.ITLS Candidate Presentation
Using Computational Tools to Understand Students’ Sociocultural Learning in Technologically-Mediated Learning Environments
Indiana University Bloomington
Thursday, 17 November, Noon-1pm, EDUC 282
With the goal of advancing our understanding of, and to better optimize support for, how people learn, the use of new technologies that reliably detect fine-grained patters of student actions are gaining traction in the learning sciences. In particular, with computational tools that capture large amounts of learning traces, and analytic techniques that help examine them, researchers can study learning in an unobtrusive way, and look at a high-resolution picture of how the learning process actually occurs. From my own ongoing research, I bring three examples to illustrate how computational tools can be used to study the learning that takes place in technologically-enhanced learning environments. In the first study, I demonstrate the use of sensing technologies to design, and monitor, embodied activities with elementary students that leverage physical movement to support the learning of scientific concepts in the context of ecosystems. Analyses of students’ interviews and log data show that students can learn from such embodied activities, and also that distinct patterns in students’ physical movements are associated with students’ conceptual understanding and learning gains. The second study is an ongoing research in an introductory undergraduate engineering course where we make use of a text-mining approach. We use text-mining algorithms to understand and assess how collaboration impacts the quality of students’ answers within Peer Investigation Groups (PIGs) across various problem-based learning units. In mining students’ text-based data (i.e., PIG artifacts and collaboration transcripts) and comparing them to various expert benchmarks, we are able to visualize the development of disciplinary discourse, and the particular moments in which the group approaches to a more normative way of talking about the problem at hand. Finally, I will briefly introduce a study, currently under design, in which we would like to understand, from a sociocultural framework, students’ activities, attention, and emotional states in face-to-face learning interactions (e.g., a classroom or a museum exhibit) by tracking their physical movements and facial expressions using analytical tools based on computer vision. In concluding, I will discuss the instructional implications of these computational tools for the design of new, technologically-enhanced learning environments.