We investigate how to use AI-based analytics to support design education. The
analytics at hand measure multiscale design, that is, students' use of space
and scale to visually and conceptually organize their design work. With the
goal of making the analytics intelligible to instructors, we developed a
research artifact integrating a design analytics dashboard with design
instances, and the design environment that students use to create them. We
theorize about how Suchman's notion of mutual intelligibility requires
contextualized investigation of AI in order to develop findings about how
analytics work for people. We studied the research artifact in 5 situated
course contexts, in 3 departments. A total of 236 students used the multiscale
design environment. The 9 instructors who taught those students experienced the
analytics via the new research artifact.
We derive findings from a qualitative analysis of interviews with instructors
regarding their experiences. Instructors reflected on how the analytics and
their presentation in the dashboard have the potential to affect design
education. We develop research implications addressing: (1) how indexing design
analytics in the dashboard to actual design work instances helps design
instructors reflect on what they mean and, more broadly, is a technique for how
AI-based design analytics can support instructors' assessment and feedback
experiences in situated course contexts; and (2) how multiscale design
analytics, in particular, have the potential to support design education. By
indexing, we mean linking which provides context, here connecting the numbers
of the analytics with visually annotated design work instances.

使用人工智能分析来支持设计教育，并通过开发一个设计分析仪表板与设计环境的研究工件，对教师的认知进行量化分析并提出了两个研究意义，探讨了设计教育中索引设计分析和多尺度设计分析的潜力。

将分析索引到实例：如何通过集成仪表板支持设计教育

Indexing Analytics to Instances: How Integrating a Dashboard can Support  Design Education

In the wake of information overload in academia, methodologies and systems
for search, recommendation, and prediction to aid researchers in identifying
relevant research are actively studied and developed. Existing work, however,
is limited in terms of granularity, focusing only on the level of papers or a
single type of artifact, such as data sets. To enable more holistic analyses
and systems dealing with academic publications and their content, we propose
CoCon, a large scholarly data set reflecting the combined use of research
artifacts, contextualized in academic publications' full-text. Our data set
comprises 35 k artifacts (data sets, methods, models, and tasks) and 340 k
publications. We additionally formalize a link prediction task for "combined
research artifact use prediction" and provide code to utilize analyses of and
the development of ML applications on our data. All data and code is publicly
available at this https URL.

本文提出了 CoCon 数据集，该数据集包含了 35K 份数据集，方法，模型和任务，并将它们联系在 340k 篇学术出版物的全文中，进一步提出了一种用于预测 “组合研究工件使用” 的链接的任务，并提供了利用数据分析和机器学习开发代码的链接。