96 research outputs found
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CloudBooks: An infrastructure for reading on multiple devices
The use of light, portable devices such as iPads whose reading angle is readily changed is radically different to reading on a desktop or laptop. However, it would be naive to view this as mere evolution. Rather, such devices permit reading activity to more closely mirror paper. A light, keyboardless device can be used in many different locations and orientations. This paper reports an infrastructure for supporting reading on multiple slate devices using a single cloud-based system to provide for numerous configurations
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems
We explore trust in a relatively new area of data science: Automated Machine
Learning (AutoML). In AutoML, AI methods are used to generate and optimize
machine learning models by automatically engineering features, selecting
models, and optimizing hyperparameters. In this paper, we seek to understand
what kinds of information influence data scientists' trust in the models
produced by AutoML? We operationalize trust as a willingness to deploy a model
produced using automated methods. We report results from three studies --
qualitative interviews, a controlled experiment, and a card-sorting task -- to
understand the information needs of data scientists for establishing trust in
AutoML systems. We find that including transparency features in an AutoML tool
increased user trust and understandability in the tool; and out of all proposed
features, model performance metrics and visualizations are the most important
information to data scientists when establishing their trust with an AutoML
tool.Comment: IUI 202
Researching AI Legibility Through Design
Everyday interactions with computers are increasingly likely to involve elements of Artificial Intelligence (AI). Encompassing a broad spectrum of technologies and applications, AI poses many challenges for HCI and design. One such challenge is the need to make AIâs role in a given system legible to the user in a meaningful way. In this paper we employ a Research through Design (RtD) approach to explore how this might be achieved. Building on contemporary concerns and a thorough exploration of related research, our RtD process reflects on designing imagery intended to help increase AI legibility for users. The paper makes three contributions. First, we thoroughly explore prior research in order to critically unpack the AI legibility problem space. Second, we respond with design proposals whose aim is to enhance the legibility, to users, of systems using AI. Third, we explore the role of design-led enquiry as a tool for critically exploring the intersection between HCI and AI research
AI-Driven Assessment of Students: Current Uses and Research Trends
During the last decade, the use of AIs is being incorporated into the
educational field whether to support the analysis of human behavior in teachinglearning
contexts, as didactic resource combined with other technologies or as a
tool for the assessment of the students.
This proposal presents a Systematic Literature Review and mapping study on
the use of AIs for the assessment of students that aims to provide a general
overview of the state of the art and identify the current areas of research by
answering 6 research questions related with the evolution of the field, and the
geographic and thematic distribution of the studies.
As a result of the selection process this study identified 20 papers focused on
the research topic in the repositories SCOPUS and Web of Science from an
initial amount of 129.
The analysis of the papers allowed the identification of three main thematic
categories: assessment of student behaviors, assessment of student sentiments
and assessment of student achievement as well as several gaps in the literature
and future research lines addressed in the discussion
Renegotiation and Relative Performance Evaluation: Why an Informative Signal may be Useless
Although Holmström's informativeness criterion provides a theoretical foundation for the controllability principle and inter firm relative performance evaluation, empirical and field studies provide only weak evidence on such practices. This paper refines the traditional informativeness criterion by abandoning the conventional full-commitment assumption. With the possibility of renegotiation, a signal's usefulness in incentive contracting depends on its information quality, not simply on whether the signal is informative. This paper derives conditions for determining when a signal is useless and when it is useful. In particular, these conditions will be met when the signal's information quality is either sufficiently poor or sufficiently rich
Crowdsourcing the Perception of Machine Teaching
Teachable interfaces can empower end-users to attune machine learning systems
to their idiosyncratic characteristics and environment by explicitly providing
pertinent training examples. While facilitating control, their effectiveness
can be hindered by the lack of expertise or misconceptions. We investigate how
users may conceptualize, experience, and reflect on their engagement in machine
teaching by deploying a mobile teachable testbed in Amazon Mechanical Turk.
Using a performance-based payment scheme, Mechanical Turkers (N = 100) are
called to train, test, and re-train a robust recognition model in real-time
with a few snapshots taken in their environment. We find that participants
incorporate diversity in their examples drawing from parallels to how humans
recognize objects independent of size, viewpoint, location, and illumination.
Many of their misconceptions relate to consistency and model capabilities for
reasoning. With limited variation and edge cases in testing, the majority of
them do not change strategies on a second training attempt.Comment: 10 pages, 8 figures, 5 tables, CHI2020 conferenc
Intelligent analysis and data visualisation for teacher assistance tools: the case of exploratory learning
While it is commonly accepted that Learning Analytics tools can support teachersâ awareness and classroom orchestration, not all forms of pedagogy are congruent to the types of data generated by digital technologies or the algorithms used to analyse them. One such pedagogy that has been so far underserved by Learning Analytics is exploratory learning, exemplified by tools such as simulators, virtual labs, microworlds and some interactive educational games. This paper argues that the combination of intelligent analysis of interaction data from such an exploratory learning environment (ELE) and the targeted design of visualisations has the benefit of supporting classroom orchestration and consequently enabling the adoption of this pedagogy to the classroom. We present a case study of learning analytics in the context of an ELE supporting the learning of algebra. We focus on the formative qualitative evaluation of a suite of Teacher Assistance tools. We draw conclusions relating to the value of the tools to teachers and reflect with transferable lessons for future related work
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