Jeremy Gow

Bio: Jeremy Gow is an academic researcher from Goldsmiths, University of London. The author has contributed to research in topics: Digital library & Game design. The author has an hindex of 18, co-authored 54 publications receiving 3904 citations. Previous affiliations of Jeremy Gow include University College London & University of London.

Cognitive economy and satisficing in information seeking: A longitudinal study of undergraduate information behavior

Abstract: This article reports on a longitudinal study of information seeking by undergraduate information management students. It describes how they found and used information, and explores their motivation and decision making. We employed a use-in-context approach where students were observed conducting, and were interviewed about, information-seeking tasks carried out during their academic work. We found that participants were reluctant to engage with a complex range of information sources, preferring to use the Internet. The main driver for progress in information seeking was the immediate demands of their work (e.g., assignments). Students used their growing expertise to justify a conservative information strategy, retaining established strategies as far as possible and completing tasks with minimum information-seeking effort. The time cost of using library material limited the uptake of such resources. New methods for discovering and selecting information were adopted only when immediately relevant to the task at hand, and tasks were generally chosen or interpreted in ways that minimized the need to develop new strategies. Students were driven by the demands of the task to use different types of information resources, but remained reluctant to move beyond keyword searches, even when they proved ineffective. They also lacked confidence in evaluating the relative usefulness of resources. Whereas existing literature on satisficing has focused on stopping conditions, this work has highlighted a richer repertoire of satisficing behaviors. © 2009 Wiley Periodicals, Inc.

An examination of the physical and the digital qualities of humanities research

Abstract: Traditionally humanities scholars have worked in physical environments and with physical artefacts. Libraries are familiar places, built on cultural traditions over thousands of years, and books are comfortable research companions. Digital tools are a more recent addition to the resources available to a researcher. This paper explores both the physical and the digital qualities of modern humanities research, drawing on existing literature and presenting a study of humanities scholars' perceptions of the research resources they use. We highlight aspects of the physical and digital that can facilitate or hinder the researcher, focusing on three themes that emerge from the data: the working environment; the experience of finding resources; and the experience of working with documents. Rather than aiming to replace physical texts and libraries by digital surrogates, providers need to recognise the complementary roles they play: digital information environments have the potential to provide improved access and analysis features and the facility to exploit the library from any place, while the physical library and resources provide greater authenticity, trustworthiness and the demand to be in a particular place with important material properties.

A library or just another information resource? A case study of users' mental models of traditional and digital libraries

Abstract: A user's understanding of the libraries they work in, and hence of what they can do in those libraries, is encapsulated in their "mental models" of those libraries. In this article, we present a focused case study of users' mental models of traditional and digital libraries based on observations and interviews with eight participants. It was found that a poor understanding of access restrictions led to risk-averse behavior, whereas a poor understanding of search algorithms and relevance ranking resulted in trial-and-error behavior. This highlights the importance of rich feedback in helping users to construct useful mental models. Although the use of concrete analogies for digital libraries was not widespread, participants used their knowledge of Internet search engines to infer how searching might work in digital libraries. Indeed, most participants did not clearly distinguish between different kinds of digital resource, viewing the electronic library catalogue, abstracting services, digital libraries, and Internet search engines as variants on a theme.

Mechanic miner: reflection-driven game mechanic discovery and level design

Abstract: We introduce Mechanic Miner, an evolutionary system for discovering simple two-state game mechanics for puzzle platform games. We demonstrate how a reflection-driven generation technique can use a simulation of gameplay to select good mechanics, and how the simulation-driven process can be inverted to produce challenging levels specific to a generated mechanic. We give examples of levels and mechanics generated by the system, summarise a small pilot study conducted with example levels and mechanics, and point to further applications of the technique, including applications to automated game design.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Modern Applied Statistics With S

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A Tutorial on UPPAAL

Abstract: This is a tutorial paper on the tool Uppaal. Its goal is to be a short introduction on the flavor of timed automata implemented in the tool, to present its interface, and to explain how to use the tool. The contribution of the paper is to provide reference examples and modeling patterns.