The article reports a field study which examined the mental models of 80 undergraduates seeking information for either a history or psychology course essay when they were in an early, exploration stage of researching their essay. This group is presently at a disadvantage when using thesaurus-type schemes in indexes and online search engines because there is a disconnect between how domain novice users of IR systems represent a topic space and how this space is represented in the standard IR system thesaurus. The study attempted to (a) ascertain the coding language used by the 80 undergraduates in the study to mentally represent their topic and then (b) align the mental models with the hierarchical structure found in many thesauri. The intervention focused the undergraduates' thinking about their topic from a topic statement to a thesis statement. The undergraduates were asked to produce three mental model diagrams for their real-life course essay at the beginning, middle, and end of the interview, for a total of 240 mental model diagrams, from which we created a 12-category mental model classification scheme. Findings indicate that at the end of the intervention, (a) the percentage of vertical mental models increased from 24 to 35p of all mental models; but that (b) 3rd-year students had fewer vertical mental models than did 1st-year undergraduates in the study, which is counterintuitive. The results indicate that there is justification for pursuing our research based on the hypothesis that rotating a domain novice's mental model into a vertical position would make it easier for him or her to cognitively connect with the thesaurus's hierarchical representation of the topic area. © 2007 Wiley Periodicals, Inc.

A classification of mental models of undergraduates seeking information for a course essay in history and psychology: Preliminary investigations into aligning their mental models with online thesauri

Frontispiece - Derek J de Solla Price Introduction - Christine L Borgman PART ONE: THEORY AND PERSPECTIVE Understanding Science - Belver C Griffith Studies of Communication and Information Disciplinary Work and Interdisciplinary Areas - Sydney J Pierce Sociology and Bibliometrics Reconciling Structure and Process in the Study of Scholarly Communication - Leah A Lievrouw PART TWO: BIBLIOMETRIC RESEARCH METHODS A View of Studies on Bibliometrics and Related Subjects in Japan - S Miyamoto, N Midorikawa and K Nakayama Author Co-citation Studies - Howard D White Overview and Defense Who Carries the Field? Communication Between Literary Schools and Critics - Karl Erik Rosengren The Absence of Co-citation as a Clue to Undiscovered Causal Connections - Don R Swanson Hierarchies and Clusters Among Communication and Library and Information Science Journals, 1977 - 1987 - Ronald E Rice PART THREE: EMPIRICAL STUDIES An Author Co-citation Analysis of Two Research Traditions - Everett M Rogers and Charlotte A Cottrill Technology Transfer and the Diffusion of Innovations A Co-citation Study of AIDS Research - Henry Small and Edwin Greenlee Mapping Authors in Intellectual Space - Katherine W McCain Population Genetics in the 1980s International Scientific Cooperation and Awareness - H F Moed and R E DeBruin A Bibliometric Case Study of Agricultural Research Within the European Community Core Journals of the Rapidly Changing Research Front of "Superconductivity" - Terrance A Brooks Editors-in-Chief of Medical Journals - S Zsindely and A Schubert Identifying the Important Theorists of Communication - James R Beniger Use of Latent Measures to Test Manifest Assumptions in Scholarly Communication Conclusions - William Paisley The Future of Bibliometrics

/pdf/scholarly-communication-and-bibliometrics-36rmhm4g54.pdf

Scholarly Communication and Bibliometrics

One distinctive feature of any adaptive system is the user model that represents essential information about each user This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user modeling

User models for adaptive hypermedia and adaptive educational systems

The Turn: Integration of Information Seeking and Retrieval in Context (The Information Retrieval Series)

Name ambiguity in the context of bibliographic citations is a difficult problem which, despite the many efforts from the research community, still has a lot of room for improvement In this article, we present a heuristic-based hierarchical clustering method to deal with this problem The method successively fuses clusters of citations of similar author names based on several heuristics and similarity measures on the components of the citations (eg, coauthor names, work title, and publication venue title) During the disambiguation task, the information about fused clusters is aggregated providing more information for the next round of fusion In order to demonstrate the effectiveness of our method, we ran a series of experiments in two different collections extracted from real-world digital libraries and compared it, under two metrics, with four representative methods described in the literature We present comparisons of results using each considered attribute separately (ie, coauthor names, work title, and publication venue title) with the author name attribute and using all attributes together These results show that our unsupervised method, when using all attributes, performs competitively against all other methods, under both metrics, loosing only in one case against a supervised method, whose result was very close to ours Moreover, such results are achieved without the burden of any training and without using any privileged information such as knowing a priori the correct number of clusters © 2010 Wiley Periodicals, Inc

An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations

The balance scorecard: Translating strategy into action: by Robert S. Kaplan and David P. Norton. Boston, MA: Harvard Business School Press, 1996. 322 p. $29.95. ISBN 0-87584-651-3

Individual differences between users of information systems can influence search performance. In user-centered design it is important to match users with system configurations that will optimize their performance. Two matching strategies were explored in the first experiment: the capitalization match, and the compensatory match. Findings suggest that a compensatory match is likely to be encountered more frequently in designing information systems. Having determined an optimal match between users and system configurations, it is necessary to find ways to ensure that users are guided to the appropriate configuration. The second experiment examined user selection of system configurations, and concluded that users do not act to optimize system configuration when they select features. This result suggests that information systems must have mechanisms such as user models to direct users to optimal configurations. These experiments suggest some of the complexities and problems encountered in applying individual differences research to user-centered design of information systems.

Individual differences and the conundrums of user-centered design: two experiments

Leadership: Theory and practice - by Peter G. Northhouse. Thousand Oaks, CA: Sage, 1997. 274p. $22.95 (paper). ISBN 0-8039-5769-6.

Person and context in information seeking: interactions between cognitive and task variables

Peter Drucker on the profession of management

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Bryce Allen

Author Tools

Chat about Author