Driving Interdisciplinary Collaboration through Adapted Conjecture Mapping: A Case Study with the PECAS Mediator

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dc.contributor.author Chang, Michael Alan
dc.contributor.author Magana, Alejandra
dc.contributor.author Benes, Bedrich
dc.contributor.author Kao, Dominic
dc.contributor.author Fusco, Judith
dc.date.accessioned 2022-05-31T18:02:30Z
dc.date.available 2022-05-31T18:02:30Z
dc.date.issued 2022-05
dc.identifier.other DOI: https://doi.org/10.51388/20.500.12265/156
dc.identifier.uri http://hdl.handle.net/20.500.12265/156
dc.description.abstract In this report, we demonstrate how an interdisciplinary team of computer science and learning sciences researchers utilize an adapted conjecture mapping tool during a collaborative problem-solving session. The session is documented through an edited “Dialogue” format, which captures the process of conjecture map construction and subsequent reflection. We find that creating the conjecture map collaboratively surfaces a key tension: while learning sciences theory often highlights the nuanced and complex relational nature of learning, even the most cutting-edge computing techniques struggle to discern these nuances. Articulating this tension proved to be highly generative, enabling the researchers to discuss how considering impacted community members as a critical “part of the solution” may lead to a socio-technical tool which supports desired learning outcomes, despite limitations in learning theory and technical capability. Ultimately, the process of developing the conjecture map directed researchers towards a precise discussion about how they would need to engage impacted community members (e.g., teachers) in a co-design process. en_US
dc.description.sponsorship This material is based upon work supported by the National Science Foundation under grant 2021159. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. en_US
dc.language.iso en_US en_US
dc.publisher Digital Promise en_US
dc.subject conjecture maps en_US
dc.subject collaboration en_US
dc.subject learning sciences en_US
dc.subject computer science en_US
dc.subject design-based research en_US
dc.subject artificial intelligence en_US
dc.subject interdisciplinary en_US
dc.title Driving Interdisciplinary Collaboration through Adapted Conjecture Mapping: A Case Study with the PECAS Mediator en_US
dc.type Technical Report en_US

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