Standing on the schemas of giants

Abstract

People spend an enormous amount of time searching for complex information online; for example, consumers researching new purchases or patients learning about their conditions. As they search, people build up rich mental schemas about their target domains; which, if effectively shared, could accelerate learning for others with similar interests. In this paper we introduce a novel approach for integrating the schemas individuals develop as they gather information online and surfacing them for others with similar interests. Through a controlled experiment we show that having access to others' schemas while foraging for information helps new users to induce more useful, prototypical, and better-structured schemas than gathering information alone.

BackTrace: A Human-AI Collaborative Approach to Discovering Studio Backdrops in Historical Photographs

“…We also note a varied taxonomy in participants' textual metadata, such as backdrop names and descriptions. These metadata adequately described the contents of the backdrops despite their unstructured nature (Kittur et al 2014). Moving forward, these descriptions can be processed and converted into more uniform annotations to improve text search functionalities.…”

A Browser Extension for in-place Signaling and Assessment of Misinformation

“…The sensemaking brought about by our tool is in fact distributed-i.e., it leverages other users' work without those users directly collaborating with each other [33]. Research has shown that empowering users to scaffold on each others' learning can increase the depth of their sensemaking [59]. Of the other work that similar to our case, aid users in making sense of information, Goyal et al provided a sensemaking translucence interface for crime solving, which included a hypothesis window to promote idea exchange and a suspect visualization for automatic feedback on suspects discussed in the hypothesis window [39].…”

3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration Design

“…Social information foraging has largely influenced in designing CSCW applications, including collaborative information seeking [31,32], collaborative data analytics [69], collaborative search [45], social network question and answering [46], and beyond [9,70]. 3D printing troubleshooting is achieved largely by active interaction between a novice and an advanced users in a remote setting using the information repository built based on social annotation [40]. On top of the established social annotation, novices achieve their sensemaking goals by defining search keywords, reading articles, and refining keywords to follow up to gradually develop one's internal representation.…”

“…An alternative resource is online communities, such as Thingiverse forums [38], 3DPrinting subreddit [64], and Stack Overflow [1] where group members post questions to seek help from advanced users. While online archives present comprehensive failure types and solutions, it can be costly for novice users to build up their schematic understanding of 3D printing troubleshooting to digest information [40,56]. Meanwhile, while online communities can provide a tailored answer that can fit the individual's specific condition, finding the right solution that can work for remote novices can be uncertain.…”

“…Based on the insights found in S1, we designed 3DPFIX, a system that provides an AI-driven "curated" troubleshooting workflow in detecting the failure type and finding a solution. In building 3DPFIX 11:3 our design, 3DPFIX transfers social annotation [40]-the knowledge accumulated in online communities contributed by their community members-into intelligent and interactive troubleshooting guidance powered by AIs to significantly reduce novices' effort for accomplishing troubleshooting. First, to leverage social annotation in designing 3DPFIX, we extracted information accumulated in the 3D printing community relevant for automating the Q&A process, such as user-posted photos showing the failed print and textual discussion threads to diagnose/resolve the failure and implement failure type classification models.…”