This NSF EEC EAGER research project investigates how undergraduate STEM and engineering students’ learning trajectories evolve over time, from 1st year to senior year, along a novice to expert spectrum. We borrow the idea of “learning trajectories” from mathematics education that can paint the evolution of students’ knowledge and skills over time over a set of learning experiences. We use a theoretical framework based on adaptive expertise and design thinking adaptive expertise to further advance a design learning continuum.
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Comparison and validation of stochastic microstructure characterization and reconstruction: Machine learning vs. deep learning methodologies
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This paper looks at technological advances in the collection and use of information about learning. Updating earlier discussions on transcripts and videorecording, the project used for examples in this paper features a digital simulation and pedagogical patterns where students met through videoconferencing as a post-pandemic alternative to table-base groupwork and then submitted transcripts of the meeting for evaluation and feedback. The transcripts were computationally analyzed to produce data streams showing the shape of conversations. These data were combined with records of students working on collaborative documents and the learning analytics for a digital simulation to illustrate new possibilities to depict collaborative student activity. Congruent with prior reflections on transcription of recordings and the recording process, this paper highlights the ways, old and new, the inscriptional process is not theory-neutral. It privileges certain activities and plays an agentive evidentiary role.more » « less
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