The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms

Recently, the HCI community has seen increased interest in the design of teaching augmentation (TA): tools that extend and complement teachers' pedagogical abilities during ongoing classroom activities. Examples of TA systems are emerging across multiple disciplines, taking various forms: e.g., ambient displays, wearables, or learning analytics dashboards. However, these diverse examples have not been analyzed together to derive more fundamental insights into the design of teaching augmentation. Addressing this opportunity, we broadly synthesize existing cases to propose the TA framework. Our framework specifies a rich design space in five dimensions, to support the design and analysis of teaching augmentation. We contextualize the framework using existing designs cases, to surface underlying design trade-offs: for example, balancing actionability of presented information with teachers' needs for professional autonomy, or balancing unobtrusiveness with informativeness in the design of TA systems. Applying the TA framework, we identify opportunities for future research and design.Comment: to be published in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 17 pages, 10 figure

Search for long-lived neutral particles in pp collisions at s√=13 TeV that decay into displaced hadronic jets in the ATLAS calorimeter

This paper describes a search for pairs of neutral, long-lived particles decaying in the ATLAS calorimeter. Long-lived particles occur in many extensions to the Standard Model and may elude searches for new promptly decaying particles. The analysis considers neutral, long-lived scalars with masses between 5 and 400 GeV, produced from decays of heavy bosons with masses between 125 and 1000 GeV, where the long-lived scalars decay into Standard Model fermions. The analysis uses either 10.8 fb−1 or 33.0 fb−1 of data (depending on the trigger) recorded in 2016 at the LHC with the ATLAS detector in proton–proton collisions at a centre-of-mass energy of 13 TeV. No significant excess is observed, and limits are reported on the production cross section times branching ratio as a function of the proper decay length of the long-lived particles

A computational model of second-order social reasoning

This paper presents the first computational cognitive model of second-order social reasoning. The model uses a decision tree strategy to reason about the opponent's behavior. We hypothesize that a decision tree strategy requires (1) declarative memory, and (2) working memory. Declarative memory is required to retrieve successive reasoning steps, while working memory is required to temporarily store these reasoning steps while the next step is retrieved from memory. The model fit on data from a social reasoning game supports the validity of the model. This initial result leads to an explicit prediction for an experiment in which the reasoning game is combined with another task that requires the same cognitive resources as hypothesized by the model. This work is a first step towards understanding higher-order social reasoning from a cognitive modeling perspective