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State-of-the-art in network science of teams offers effective recommendation methods to answer questions like who is the best replacement, what is the best team expansion strategy, but lacks intuitive ways to explain why the optimization algorithm gives the specific recommendation for a given team optimization scenario. To tackle this problem, we develop an interactive prototype system, Extra, as the first step towards addressing such a sense-making challenge, through the lens of the underlying network where teams embed, to explain the team recommendation results. The main advantages are (1) Algorithm efficacy: we propose an effective and fast algorithm to explain random walk graph kernel, the central technique for networked team recommendation; (2) Intuitive visual explanation: we present intuitive visual analysis of the recommendation results, which can help users better understand the rationality of the underlying team recommendation algorithm.
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Embedding Computational Thinking in the Elementary Classroom: An Extended Collaborative Teacher Learning Experience
Abstract: We used design-based research to investigate an extended professional learning experience to prepare teachers to embed computational thinking in elementary science. Opportunities to interact synchronously in a community of practice - including through in person engagement in embodied challenges, discussion, and resource sharing, appeared to productively support teacher preparedness to embed CT in their science teaching. However, asynchronous collaboration via an online platform was less effective. We describe planned adjustments for future iterations of the program.
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- Award ID(s):
- 1639891
- PAR ID:
- 10124817
- Date Published:
- Journal Name:
- Computer-supported collaborative learning
- ISSN:
- 1573-4552
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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