Construction and abstraction: contrasting methods of supporting model building in learning science

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A study of interface support mechanisms for interactive information retrieval

Advances in search technology have meant that search systems can now offer assistance to users beyond simply retrieving a set of documents. For example, search systems are now capable of inferring user interests by observing their interaction, offering suggestions about what terms could be used in a query, or reorganizing search results to make exploration of retrieved material more effective. When providing new search functionality, system designers must decide how the new functionality should be offered to users. One major choice is between (a) offering automatic features that require little human input, but give little human control; or (b) interactive features which allow human control over how the feature is used, but often give little guidance over how the feature should be best used. This article presents a study in which we empirically investigate the issue of control by presenting an experiment in which participants were asked to interact with three experimental systems that vary the degree of control they had in creating queries, indicating which results are relevant in making search decisions. We use our findings to discuss why and how the control users want over search decisions can vary depending on the nature of the decisions and the impact of those decisions on the user's search

Complexity models in design

Complexity is a widely used term; it has many formal and informal meanings. Several formal models of complexity can be applied to designs and design processes. The aim of the paper is to examine the relation between complexity and design. This argument runs in two ways. First designing provides insights into how to respond to complex systems – how to manage, plan and control them. Second, the overwhelming complexity of many design projects lead us to examine how better understanding of complexity science can lead to improved designs and processes. This is the focus of this paper. We start with an outline of some observations on where complexity arises in design, followed by a brief discussion of the development of scientific and formal conceptions of complexity. We indicate how these can help in understanding design processes and improving designs

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.

We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Teaching varies with task complexity in wild chimpanzees

Understanding social influences on how apes acquire tool behaviors can help us model the evolution of culture and technology in humans. Humans scaffold novice tool skills with diverse strategies, including the transfer of tools between individuals. Chimpanzees transfer tools, and this behavior meets criteria for teaching. However, it is unclear how task complexity relates to this form of helping. Here, we find differences between 2 wild chimpanzee populations in rate, probability, and types of tool transfer during termite gathering. Chimpanzees showed greater helping in the population where termite gathering is a more complex tool task. In wild chimpanzees, as in humans, regular and active provisioning of learning opportunities may be essential to the cultural transmission of complex skills.Cumulative culture is a transformative force in human evolution, but the social underpinnings of this capacity are debated. Identifying social influences on how chimpanzees acquire tool tasks of differing complexity may help illuminate the evolutionary origins of technology in our own lineage. Humans routinely transfer tools to novices to scaffold their skill development. While tool transfers occur in wild chimpanzees and fulfill criteria for teaching, it is unknown whether this form of helping varies between populations and across tasks. Applying standardized methods, we compared tool transfers during termite gathering by chimpanzees in the Goualougo Triangle, Republic of Congo, and in Gombe, Tanzania. At Goualougo, chimpanzees use multiple, different tool types sequentially, choose specific raw materials, and perform modifications that improve tool efficiency, which could make it challenging for novices to manufacture suitable tools. Termite gathering at Gombe involves a single tool type, fishing probes, which can be manufactured from various materials. Multiple measures indicated population differences in tool-transfer behavior. The rate of transfers and probability of transfer upon request were significantly higher at Goualougo, while resistance to transfers was significantly higher at Gombe. Active transfers of tools in which possessors moved to facilitate possession change upon request occurred only at Goualougo, where they were the most common transfer type. At Gombe, tool requests were typically refused. We suggest that these population differences in tool-transfer behavior may relate to task complexity and that active helping plays an enhanced role in the cultural transmission of complex technology in wild apes

Effects of alarms on control of robot teams

Annunciator driven supervisory control (ADSC) is a widely used technique for directing human attention to control systems otherwise beyond their capabilities. ADSC requires associating abnormal parameter values with alarms in such a way that operator attention can be directed toward the involved subsystems or conditions. This is hard to achieve in multirobot control because it is difficult to distinguish abnormal conditions for states of a robot team. For largely independent tasks such as foraging, however, self-reflection can serve as a basis for alerting the operator to abnormalities of individual robots. While the search for targets remains unalarmed the resulting system approximates ADSC. The described experiment compares a control condition in which operators perform a multirobot urban search and rescue (USAR) task without alarms with ADSC (freely annunciated) and with a decision aid that limits operator workload by showing only the top alarm. No differences were found in area searched or victims found, however, operators in the freely annunciated condition were faster in detecting both the annunciated failures and victims entering their cameras' fields of view. Copyright 2011 by Human Factors and Ergonomics Society, Inc. All rights reserved