Automated essay scoring (AES) allows writing to be assigned in large courses and can provide instant formative feedback to students. However, creating models for AES can be costly, requiring the collection and human scoring of hundreds of essays. We have developed and are piloting a web-based tool that allows instructors to incrementally score responses to enable AES scoring while minimizing the number of essays the instructors must score. Previous work has shown that techniques from the machine learning subfield of active learning can reduce the amount of training data required to create effective AES models. We extend those results to a less idealized scenario: one driven by the instructor's need to score sets of essays, in which the model is trained iteratively using batch mode active learning. We propose a novel approach inspired by a class of topological methods, but with reduced computational requirements, which we refer to as topological maxima. Using actual student data, we show that batch mode active learning is a practical approach to training AES models. Finally, we discuss implications of using this technology for automated customized scoring of writing across the curriculum.

Public transportation systems are among the most ubiquitous and complex large-scale systems found in modern society. For those unable to drive such as people with cognitive disabilities, these systems are essential gateways for participation in community activities, socialization, and independence. To understand the magnitude and scope of this national problem, we highlight deficiencies identified in an international study by the Transportation Research Board of the National Research Council and present specific cognitive barriers identified in empirical studies of transportation systems in several U.S. cities.An interdisciplinary team of HCI researchers, urban transportation planners, commercial technologists, and assistive care specialists are now collaborating on the Mobility-for-All project to create architectures and prototypes that support those with cognitive disabilities and their caregivers. We have grounded our research and design efforts using a distributed cognition framework. We have derived requirements for our designs by analyzing “how things are” for individuals with cognitive disabilities who learn and use public transportation systems. We present a socio-technical architecture that has three components: a) a personal travel assistant that uses real-time Global Positioning Systems data from the bus fleet to deliver just-in-time prompts; b) a mobile prompting client and a prompting script configuration tool for caregivers; and c) a monitoring system that collects real-time task status from the mobile client and alerts the support community of potential problems. We then describe a phased community-centered assessment approach that begins at the design stage and continues to be integrated throughout the project.This research has broad implications for designing more human-centered transportation systems that are universally accessible for other disenfranchised communities such as the elderly or nonnative speaker. This project presents an “in-the-world” research opportunity that challenges our understanding about mobile human computer interactions with ubiquitous, context-aware computing architectures in noisy, uncontrolled environments; personalization and user modeling techniques; and the design of universally accessible interfaces for complex systems through participatory design processes.This article provides both a near-term vision and an architecture for transportation systems that are socially inclusive, technologically appealing, and easier for everyone to use.

In this paper we describe a current group configuration that is used to support people with cognitive disabilities (hereinafter referred to as “clients”) in the workplace. A client receiving face-to-face, often one-to-one assistance from a dedicated human job coach is characteristic of this “traditional ” model. We compare this traditional model with other group configurations that are used in cooperative and distributed work practices. In so doing, we highlight requirements that are unique to task support for people with cognitive disabilities. A survey of technologies that have been developed to provide clients with greater levels of independence is then presented. These endeavors often attempt to replace human job coaches with computational cognitive aids. We discuss some limitations of such approaches and then present a model and prototype that extends the computational job coach by incorporating human caregivers in a distributed one-to-many support system.

Complex design problems require more knowledge than any single person possesses because the knowledge relevant to a problem is usually distributed among stakeholders. Bringing different and often controversial points of view together to create a shared understanding among these stakeholders can lead to new insights, new ideas, and new artifacts. New media that allow owners of problems to contribute to framing and resolving complex design problems can extend the power of the individual human mind. Based on our past work and study of other approaches, systems, and collaborative and participatory processes, this article identifies challenges we see as the limiting factors for future collaborative human-computer systems. The Envisionment and Discovery Collaboratory (EDC) is introduced as an integrated physical, and computational environment addressing some of these challenges. The vision behind the EDC shifts future development away from the computer as the focal point, toward an emphasis that tries to improve our understanding of the human, social, and cultural system that creates the context for use. This work is based on new conceptual principles that include creating shared understanding among various stakeholders, contextualizing information to the task at hand, and creating objects to think with in collaborative design activities. Although the EDC framework is applicable to different domains; our initial effort has focused on the domain of urban planning (specifically transportation planning) and community development.