Finding relevant publications is a common task. Typically, a researcher browses through a list of publications and traces additional relevant publications. When relevant publications are identified, the list may be expanded by the citation links of the relevant publications. The information needs of researchers may change as they go through such iterative processes. The exploration process quickly becomes cumbersome as the list expands. Most existing academic search systems tend to be limited in terms of the extent to which searchers can adapt their search as they proceed. In this article, we introduce an adaptive visual exploration system named PaperPoles to support exploration of scientific publications in a context‐aware environment. Searchers can express their information needs by intuitively formulating positive and negative queries. The search results are grouped and displayed in a cluster view, which shows aspects and relevance patterns of the results to support navigation and exploration. We conducted an experiment to compare PaperPoles with a list‐based interface in performing two academic search tasks with different complexity. The results show that PaperPoles can improve the accuracy of searching for the simple and complex tasks. It can also reduce the completion time of searching and improve exploration effectiveness in the complex task. PaperPoles demonstrates a potentially effective workflow for adaptive visual search of complex information.

This half-day tutorial aims to introduce the fundamental concepts, principles and methods of visualizing and exploring the development of a scientific knowledge domain. The tutorial explains the design rationale and various applications of CiteSpace ' a freely available tool for interactive and exploratory analysis of the evolution of a scientific domain, ranging from a single specialty to multiple interrelated scientific frontiers. The tutorial demonstrates the analytic procedure of applying CiteSpace to a diverse range of examples and how one may interpret various patterns and trends revealed by interactive visual analytics.magnetic field, applied along the easy axis of the elements.

Using visual analytic systems effectively may incur a steep learning curve for users, especially for those who have little prior knowledge of either using the tool or accomplishing analytic tasks. How do users deal with a steep learning curve over time? Are there particularly problematic aspects of an analytic process? In this article we investigate these questions through an integrative study of the use of CiteSpace—a visual analytic tool for finding trends and patterns in scientific literature. In particular, we analyze millions of interactive events in logs generated by users worldwide over a 14‐month period. The key findings are: (i) three levels of proficiency are identified, namely, level 1: low proficiency, level 2: intermediate proficiency, and level 3: high proficiency, and (ii) behavioral patterns at level 3 are resulted from a more engaging interaction with the system, involving a wider variety of events and being characterized by longer state transition paths, whereas behavioral patterns at levels 1 and 2 seem to focus on learning how to use the tool. This study contributes to the development and evaluation of visual analytic systems in realistic settings and provides a valuable addition to the study of interactive visual analytic processes.

Understanding ongoing topics and their evolutions in social media is of great importance. Although topic analysis is not a novel research question, social media environment has presented new challenges. First, with insufficient co-occurrence information, short text have undermined many word co-occurrence oriented topic models' applicability. Second, real time message streams make traditional discretized topic tracking methods hard to function. Third, topics' evolution mechanisms are of great importance in social media context, but many studies have ignored them. Forth, topics have more complicated correlation among each other. Considering the existing problems, this paper has proposed a Semi-Supervised Dirichlet-Hawkes Process (SDHP) to deal with topic detection and tracking from social media. The main contributions of this paper are reflected in: (1) SDHP can handle short text problem efficiently; (2) SDHP can track topics from continuous message stream; (3) SDHP can reveal topics' underlying evolution patterns; and (4) SDHP can capture topics' correlations We have evaluated SDHP's ability in both topic detection and tracking in 8 real datasets from Twitter, and the algorithm's performances are very promising.

Spatially and temporally relevant text data generated on the Internet by users worldwide is of great value for investigating and understanding emerging trends of user interests and how they may evolve over time and space. However, exploring the spatiotemporal text data and characterizing the evolution of topics over time and space are challenging due to the complexity of such data and associated activities. This paper proposes a new approach to exploring the spatiotemporal text data with visual filters. We introduce a notion of topic trajectory to depict the spatiotemporal evolution of topics. Multiple coordinated visualizations provided in our visualization system enable users to explore topic trajectories and develop their contextual awareness in terms of how information flows across different regions. We demonstrate the use of our system with an analysis of a dataset contributed by users of widely used science mapping software CiteSpace.

Portfolio analysis of the publication profile of a unit of interest, ranging from individuals and organizations to a scientific field or interdisciplinary programs, aims to inform analysts and decision makers about the position of the unit, where it has been, and where it may go in a complex adaptive environment. A portfolio analysis may aim to identify the gap between the current position of an organization and a goal that it intends to achieve or identify competencies of multiple institutions. We introduce a new visual analytic method for analyzing, comparing, and contrasting characteristics of publication portfolios. The new method introduces a novel design of dual‐map thematic overlays on global maps of science. Each publication portfolio can be added as one layer of dual‐map overlays over 2 related, but distinct, global maps of science: one for citing journals and the other for cited journals. We demonstrate how the new design facilitates a portfolio analysis in terms of patterns emerging from the distributions of citation threads and the dynamics of trajectories as a function of space and time. We first demonstrate the analysis of portfolios defined on a single source article. Then we contrast publication portfolios of multiple comparable units of interest; namely, colleges in universities and corporate research organizations. We also include examples of overlays of scientific fields. We expect that our method will provide new insights to portfolio analysis.

Cocitation and co‐word methods have long been used to detect and track emerging topics in scientific literature, but both have weaknesses. Recently, while many researchers have adopted generative probabilistic models for topic detection and tracking, few have compared generative probabilistic models with traditional cocitation and co‐word methods in terms of their overall performance. In this article, we compare the performance of hierarchical Dirichlet process (HDP), a promising generative probabilistic model, with that of the 2 traditional topic detecting and tracking methods—cocitation analysis and co‐word analysis. We visualize and explore the relationships between topics identified by the 3 methods in hierarchical edge bundling graphs and time flow graphs. Our result shows that HDP is more sensitive and reliable than the other 2 methods in both detecting and tracking emerging topics. Furthermore, we demonstrate the important topics and topic evolution trends in the literature of terrorism research with the HDP method.

Retracting published scientific articles is increasingly common. Retraction is a self‐correction mechanism of the scientific community to maintain and safeguard the integrity of scientific literature. However, a retracted article may pose a profound and long‐lasting threat to the credibility of the literature. New articles may unknowingly build their work on false claims made in retracted articles. Such dependencies on retracted articles may become implicit and indirect. Consequently, it becomes increasingly important to detect implicit and indirect threats. In this article, our aim is to raise the awareness of the potential threats of retracted articles even after their retraction and demonstrate a visual analytic study of retracted articles with reference to the rest of the literature and how their citations are influenced by their retraction. The context of highly cited retracted articles is visualized in terms of a co‐citation network as well as the distribution of articles that have high‐order citation dependencies on retracted articles. Survival analyses of time to retraction and postretraction citation are included. Sentences that explicitly cite retracted articles are extracted from full‐text articles. Transitions of topics over time are depicted in topic‐flow visualizations. We recommend that new visual analytic and science mapping tools should take retracted articles into account and facilitate tasks specifically related to the detection and monitoring of retracted articles.

Using the option Analyze Results with the Web of Science, one can directly generate overlays onto global journal maps of science. The maps are based on the 10,000+ journals contained in the Journal Citation Reports (JCR) of the Science and Social Sciences Citation Indices (2011). The disciplinary diversity of the retrieval is measured in terms of Rao‐Stirling's “quadratic entropy” (Izsák & Papp, 1995). Since this indicator of interdisciplinarity is normalized between 0 and 1, interdisciplinarity can be compared among document sets and across years, cited or citing. The colors used for the overlays are based on Blondel, Guillaume, Lambiotte, and Lefebvre's (2008) community‐finding algorithms operating on the relations among journals included in the JCR. The results can be exported from VOSViewer with different options such as proportional labels, heat maps, or cluster density maps. The maps can also be web‐started or animated (e.g., using PowerPoint). The “citing” dimension of the aggregated journal–journal citation matrix was found to provide a more comprehensive description than the matrix based on the cited archive. The relations between local and global maps and their different functions in studying the sciences in terms of journal literatures are further discussed: Local and global maps are based on different assumptions and can be expected to serve different purposes for the explanation.

Although it is commonly expected that the citation context of a reference is likely to provide more detailed and direct information about the nature of a citation, few studies in the literature have specifically addressed the extent to which the information in different parts of a scientific publication differs. Do abstracts tend to use conceptually broader terms than sentences in a citation context in the body of a publication? In this article, we propose a method to analyze and compare latent topics in scientific publications, in particular, from abstracts of papers that cited a target reference and from sentences that cited the target reference. We conducted an experiment and applied topical modeling techniques to full‐text papers in eight biomedicine journals. Topics derived from the two sources are compared in terms of their similarities and broad‐narrow relationships defined based on information entropy. The results show that abstracts and citation contexts are characterized by distinct sets of topics with moderate overlaps. Furthermore, the results confirm that topics from abstracts of citing papers have broader terms than topics from citation contexts formed by citing sentences. The method and the findings could be used to enhance and extend the current methodologies for research evaluation and citation evaluation.

Abstract A critical part of a scientific activity is to discern how a new idea is related to what we know and what may become possible. As the number of new scientific publications arrives at a rate that rapidly outpaces our capacity of reading, analyzing, and synthesizing scientific knowledge, we need to augment ourselves with information that can effectively guide us through the rapidly growing intellectual space. In this article, we address a fundamental issue concerning what kinds of information may serve as early signs of potentially valuable ideas. In particular, we are interested in information that is routinely available and derivable upon the publication of a scientific paper without assuming the availability of additional information such as its usage and citations. We propose a theoretical and computational model that predicts the potential of a scientific publication in terms of the degree to which it alters the intellectual structure of the state of the art. The structural variation approach focuses on the novel boundary‐spanning connections introduced by a new article to the intellectual space. We validate the role of boundary‐spanning in predicting future citations using three metrics of structural variation—namely, modularity change rate, cluster linkage, and Centrality Divergence—along with more commonly studied predictors of citations such as the number of coauthors, the number of cited references, and the number of pages. Main effects of these factors are estimated for five cases using zero‐inflated negative binomial regression models of citation counts. Key findings indicate that (a) structural variations measured by cluster linkage are a better predictor of citation counts than are the more commonly studied variables such as the number of references cited, (b) the number of coauthors and the number of references are both good predictors of global citation counts to a lesser extent, and (c) the Centrality Divergence metric is potentially valuable for detecting boundary‐spanning activities at interdisciplinary levels. The structural variation approach offers a new way to monitor and discern the potential of newly published papers in context. The boundary‐spanning mechanism offers a conceptually simplified and unifying explanation of the roles played by commonly studied extrinsic properties of a publication in the study of citation behavior.

Abstract A multiple‐perspective cocitation analysis method is introduced for characterizing and interpreting the structure and dynamics of cocitation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Cocitation networks are decomposed into cocitation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a cocitation cluster's members and their citers. The generic method is applied to a three‐part analysis of the field of information science as defined by 12 journals published between 1996 and 2008: (a) a comparative author cocitation analysis (ACA), (b) a progressive ACA of a time series of cocitation networks, and (c) a progressive document cocitation analysis (DCA). Results show that the multiple‐perspective method increases the interpretability and accountability of both ACA and DCA networks.

Visualizing the intellectual structure of scientific domains using co-cited units such as references or authors has become a routine for domain analysis. In previous studies, paper-reference matrices are usually transformed into reference-reference matrices to obtain co-citation relationships, which are then visualized in different representations, typically as node-link networks, to represent the intellectual structures of scientific domains. Such network visualizations sometimes contain tightly knit components, which make visual analysis of the intellectual structure a challenging task. In this study, we propose a new approach to reveal co-citation relationships. Instead of using a reference-reference matrix, we directly use the original paper-reference matrix as the information source, and transform the paper-reference matrix into an FP-tree and visualize it in a Java-based prototype system. We demonstrate the usefulness of our approach through visual analyses of the intellectual structure of two domains: Information Visualization and Sloan Digital Sky Survey (SDSS). The results show that our visualization not only retains the major information of co-citation relationships, but also reveals more detailed sub-structures of tightly knit clusters than a conventional node-link network visualization.

Abstract This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time‐variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co‐citation footprint in scientific literature—an evolving network of scientific publications cited by research‐front concepts. Kleinberg's (2002) burst‐detection algorithm is adapted to identify emergent research‐front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time‐zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research‐front terms, (b) the value of a co‐citation cluster is explicitly interpreted in terms of research‐front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981–2004) and terrorism (1990–2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal‐point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.

In this paper, we describe the development of CiteSpace as an integrated environment for identifying and tracking thematic trends in scientific literature. The goal is to simplify the process of finding not only highly cited clusters of scientific articles, but also pivotal points and trails that are likely to characterize fundamental transitions of a knowledge domain as a whole. The trails of an advancing research field are captured through a sequence of snapshots of its intellectual structure over time in the form of Pathfinder networks. These networks are subsequently merged with a localized pruning algorithm. Pivotal points in the merged network are algorithmically identified and visualized using the betweenness centrality metric. An example of finding clinical evidence associated with reducing risks of heart diseases is included to illustrate how CiteSpace could be used. The contribution of the work is its integration of various change detection algorithms and interactive visualization capabilities to simply users' tasks.

Finding the most rigorous, updated, and well received clinical evidence is a crucial and challenging task in the practice of evidence-based medicine (EBM). In this article, we describe a knowledge domain visualization-based quantitative approach that is designed to support the task of searching for high-quality clinical evidence in the medical literature. We illustrate the use of this new approach with the knowledge domain of non-steroidal anti-inflammatory drugs (NSAIDs). A sample of the literature is visualized in a base map depicting structural and temporal properties of emerging themes and references made by such themes over time. In addition, the visualization highlights the rigorousness of a published clinical trial in terms of the type of study design retrieved dynamically from PubMed. The contribution of this approach is that it offers users an integrated search environment so that the rigorousness, recentness, and consensus of clinical evidence can be assessed with the support of visual exploration facilities.

Research fronts are the forefront of a scientific field timely identifying emerging trends and abrupt changes in scientific literature is not only beneficial for users of digital libraries, but also instrumental for users to trace the movement of a research front. A research front can be seen as a dynamic and transient mapping from the state of the art to its immediate prior art - the intellectual base. Citation and co-citation analysis primarily focuses on the structure and dynamics of the intellectual base, information retrieval and related research mainly focuses on intrinsic properties of text documents. In this article, we describe a novel approach that enables us to detect and visualize the transient relationship over time. Temporal associations between content words connected to a surge of interest in scientific papers and high-impact articles in the intellectual base are identified and visualized as a time-variant network. We apply the approach to the detection of thematic changes over time in information visualization and research in terrorism.

Many of us working in visualization have our own list of our top 5 or 10 unresolved problems in visualization. We have assembled a group of panelists to debate and perhaps reach concensus on the top problems in visualization that still need to be explored. We include panelists from both the information and scientific visualization domains. After our presentations, we encourage interaction with the audience to see if we can further formulate and perhaps finalize our list of top unresolved problems in visualization.

Diffusion-limited aggregation (DLA) is a model of fractal growth. Computer models can simulate the fast aggregation of millions of particles. We propose a modified version of DLA, called semantically modified DLA (SM-DLA), for visualizing large-scale networks. SM-DLA introduces similarity measures between particles so that instead of attaching to the nearest particle in the aggregation, a new particle is stochastically directed to attach to particles that are similar to it. The results of our initial experiment with a co-citation network using SM-DLA are encouraging, suggesting that the algorithm has the potential as an alternative paradigm for visualizing large-scale networks. Further studies in this direction are recommended.

Abstract Knowledge domain visualization is a visual exploratory approach to the study of the development of a knowledge domain. In this study, we focus on the practical issues concerning modeling and visualizing scientific revolutions. We study the growth patterns of specialties derived from citation and cocitation data on string theory in physics. Special attention is given to the two superstring revolutions since the 1980s. The superstring revolutions are visualized, animated, and analyzed using the general framework of Thomas Kuhn's structure of scientific revolutions. The implications of taking this approach are discussed.

Abstract In this article we demonstrate the use of an integrative approach to visualizing and tracking the development of scientific paradigms. This approach is designed to reveal the long‐term process of competing scientific paradigms. We assume that a cluster of highly cited and cocited scientific publications in a cocitation network represents the core of a predominant scientific paradigm. The growth of a paradigm is depicted and animated through the rise of citation rates and the movement of its core cluster towards the center of the cocitation network. We study two cases of competing scientific paradigms in the real world: (1) the causes of mass extinctions, and (2) the connections between mad cow disease and a new variant of a brain disease in humans—vCJD. Various theoretical and practical issues concerning this approach are discussed.

Domain visualization is one of the new research fronts resulted from the proliferation of information visualization, aiming to reveal the essence of a knowledge domain. Information visualization plays an integral role in modeling and representing intellectual structures associated with scientific disciplines. In this article, the domain of computer graphics is visualized based on author cocitation patterns derived from an 18-year span of the prestigious IEEE Computer Graphics and Applications (1982–1999). This domain visualization utilizes a series of visualization and animation techniques, including author cocitation maps, citation time lines, animation of a high-dimensional specialty space, and institutional profiles. This approach not only augments traditional domain analysis and the understanding of scientific disciplines, but also produces a persistent and shared knowledge space for researchers to keep track the development of knowledge more effectively. The results of the domain visualization are discussed and triangulated in a broader context of the computer graphics field.

Knowledge discovery and data mining commonly rely on finding salient patterns of association from a vast amount of data. Traditional citation analysis of scientific literature draws insights from strong citation patterns. Latent domain knowledge, in contrast to the mainstream domain knowledge, often consists of highly relevant but relatively infrequently cited scientific works. Visualizing latent domain knowledge presents a significant challenge to knowledge discovery and quantitative studies of science. We build upon a citation-based knowledge visualization procedure and develop an approach that not only captures knowledge structures from prominent and highly cited works, but also traces latent domain knowledge through low-frequency citation chains. We apply this approach to two cases: (1) identifying cross-domain applications of Pathfinder networks (PFNETs) and (2) clarifying the current status of scientific inquiry of a possible link between Bovine spongiform encephalopathy (BSE), also known as mad cow disease, and a new variant Creutzfeldt-Jakob disease (vCJD), a type of brain disease in humans.

Digital images and videos have an increasingly important role in today's telecommunication and our everyday life in modern information society. In this paper, we explore the synergy between content-based image retrieval (CBIR) techniques and Pathfinder networks. Salient image features, based on colour, texture, edge orientation, and edge distance, are extracted. The structural modelling capabilities of Pathfinder are then applied to simplify and visualise the strongest interrelationships in the image database.

Domain visualization aims to reveal the most significant intellectual structure associated with a subject domain. This article illustrates how domain visualization enables novel ways of accessing scientific literature in digital libraries. The domain of computer graphics is visualized based on a citation analysis of articles appeared in the prestigious IEEE Computer Graphics and Applications over 18 years (1982-1999). The derived the high-dimensional domain structure is uncovered through the use of various visualization and animation techniques. Author co-citation maps open up new ways of visual information retrieval. Not only can users explore domain visualizations as a virtual landscape, but also invoke queries directly to a digital library. In this article, the domain visualization is linked to various citation-related information of scientific literature on the Web through NEC's ResearchIndex system.

The proliferation of content based image retrieval techniques has highlighted the need to understand the relationship between image clustering based on low-level image features and image clustering made by human users. In conventional image retrieval systems, images are typically characterized by a range of features such as color, texture, and shape. However, little is known to what extent these low-level features can be effectively combined with information visualization techniques such that users may explore images in a digital library according to visual similarities. The authors compare and analyze a number of Pathfinder networks of images generated based on such features. Salient structures of images are visualized according to features extracted from color, texture, and shape orientation. Implications for visualizing and constructing hypermedia systems are discussed.

"Bringing global information systems into business." European Journal of Information Systems, 9(3), pp. 204–205

This article presents two studies concerning the role of individual differences in searching through a spatial-semantic virtual environment. In the first study, 10 subjects searched for two topics through a spatial user interface of a semantic space. A strong positive correlation was found between associative memory (MA-1) and search performance (r = 0.855, p = 0.003), but no significant correlation was found between visual memory (MV-1) and search performance. In the second study, 12 subjects participated in a within-subject experimental design. The same spatial user interface and a simple textual user interface were used. The effects of spatial ability (VZ-2), associative memory (MA-1), and on-line experience were tested on a set of interrelated search performance scores. A statistically significant main effect of on-line experience was found, F(6, 4) = 6.213, p = 0.049, two-tailed. In particular, on-line experience has a significant effect on the recall scores with the textual interface. Individuals experienced in on-line search are more likely to have a higher recall score with the textual interface than less experienced individuals. No significant main effects were found for spatial ability and associative memory. Subjects' comments suggest a potentially complex interplay between individuals' mental models and the high-dimensional semantic model. Qualitative and process-oriented studies are, therefore, called for to reveal the complex interaction between individuals' cognitive abilities, domain knowledge, and direct manipulation skills. A recommendation is made that spatial-semantic models should be adaptable to suit individuals and tasks at various levels.

The practical significance of identifying and accommodating individual differences has been established across a number of fields of research. There is a renewed interest in individual differences due to the advances in virtual environments, especially through far-reaching technologies such as information visualization and 3D graphical user interfaces on the World Wide Web. The effects of individual differences on the use of these new technologies are yet to be found out. More fundamentally, theories and methods developed for the earlier generations of information systems are subject to a close examination of their applicability, efficiency, and effectiveness. In this article, we present a brief historical overview of research in individual differences in the context of virtual environments. In particular, we highlight the notion of structure in the perception of individual users of an information system and the role of individuals' abilities to recognize and use such structures to perform various information-intensive tasks. Striking the balance between individuals' abilities and the demanding task for detecting, understanding, and utilizing such structures is an emerging theme across the five articles in this special issue. We outline the approaches and the major findings of these articles with reference to this central theme.

A semantic-centric approach to information visualization is described in this paper. In contrast to the conventional document-centric approaches, the semantic-centric approach focuses on the intrinsic connectivity in an information space beyond the boundary of individual documents. The approach aims to visualize the profound interrelationships perceived by the scientific community through the literature. An application of this approach is demonstrated through an author co-citation analysis of the entire collection of the ACM Hypertext conference proceedings over the last decade. Sub-fields, or specialties, in the field of hypertext are visualized based on the results of a factor analysis of author co-citation patterns. Nine snapshots of annual author co-citation maps introduce a new way of knowledge discovering in an information space shaped by the contemporary literature.

We explore the potential of information visualization techniques in enhancing existing methodologies for domain analysis and modeling. In this case study, we particularly focus on visualizing the evolution of the hypertext field based on author co-citation patterns, including the use of a sliding-window scheme to generate a series of annual snapshots of the domain structure, and a factor-referenced color-coding scheme to highlight predominant specialties in the field.

This paper introduces a generic approach to the development of hypermedia information systems. This approach emphasises the role of intrinsic inter-document relationships in structuring and visualising a large hypermedia information space. In this paper, we illustrate the use of this approach based on three types of similarity measurements: hypertext linkage, content similarity and usage patterns. Salient patterns in these relationships are extracted and visualised in a simple and intuitive associated network. The spatial layout of a visualisation is optimised such that closely related documents are placed near to each other and only those intrinsic connections among them are shown to users as automatically generated virtual links. This approach supports self-organised information space transformation based on usage patterns and other feedback such that the visual structure of the information space is incrementally tailored to users' search and browsing styles.

This paper presents two studies of the use of the WWW in Scottish universities and American land-grant universities. First, we investigated the relationship between the organisational profile of a university department in Scotland and its structural connectivity on the WWW. A Spearman rank order correlation analysis revealed a number of strong correlation relationships between structural connectivity measures and the organisational profile based on research assessment exercise ratings, teaching quality assessments, student–staff ratios and funding levels. Linkage patterns from 13 Scottish academic sites to commercial sites in Britain and America highlighted the impact of culture and the appropriateness of information technologies on the acceptance of the WWW. The second study is a content survey of WWW-based education activities in American land-grant universities to investigate successful applications of these enabling techniques in education. The two studies together highlighted cultural, political and technological interactions in the use of the WWW.

This work explores a novel approach to the analysis and modeling of computer supported collaborative writing. The aim of this dynamic modeling approach is to capture the structure of user behavior as they interact with a computer system. Markov chains models are derived from the empirical data on the use of the system. These models are compared so as to highlight how the dynamic structure of collaborative writing is affected by factors such as the level of users' experience with the system and workspaces for different writing projects. A workspace is associated with a collaborative writing project in terms of both its structure and content. The study shows that the patterns of interactive behaviors of the users were clearly influenced by the system design. Users' experience and workspaces both significantly affected the structure of interactive behaviors in collaborative writing. Users intensively used the system for exploration, organization, and composition tasks in collaborative writing, whereas the use of collaboration facilities of the system was transient in nature. The descriptive-prescriptive nature of the approach has the advantage of reducing the complexity of computer supported collaborative writing down to a more manageable level at which investigators can concentrate more directly on the essence of the dynamics. The experience from the study suggests that this approach is worth pursuing, and it may be fruitfully generalized by integrating with other process modeling techniques and the mainstream practice of software engineering. © 1997 John Wiley & Sons, Inc.

The meta-analysis compared and synthesized the results of 23 experimental studies on hypertext. The analysis was based on 56 pairs of effect sizes and significance levels of the impact of users, tasks, and tools on interactions with hypertext. This analysis focused on three factors that prevailingly influence the use of hypertext: the cognitive styles and spatial ability of users; the complexity of tasks; and the structure of information organization and the visualization of the structure. The meta-analysis found that this group of experimental studies reported significantly discrepant findings, indicating that substantial differences exist among individual experiments. Individual differences in cognition did not yield enough evidence to conclude that the effect sizes are significantly apart from zero. The meta-analysis showed that the overall performance of hypertext users tended to be more effective than that of nonhypertext users, but the differences in efficiency measures were consistently in favor of nonhypertext users. Users benefited more from hypertext tools for open tasks. Overall, the complexity of tasks has the largest combined effect sizes. Graphical maps that visualize the organization of hypertext have significant impact on the usefulness of a hypertext system. This meta-analysis raised two issues concerned with the present hypertext literature: (a) the absence of a taxonomy of tasks for analyzing and comparing hypertext usability across studies, and (b) the weaknesses of the connections between abstract hypertext reference models and specific hypertext systems. These weaknesses may considerably undermine the significance of individual findings on hypertext usability. Results of the meta-analysis suggest that the discrepancies among empirical findings are related to these weaknesses. Future work on hypertext usability should emphasize task taxonomies along with longitudinal and ethnographic studies for a deep understanding of the interactions between users and hypertext. Recommended research issues for the future are highlighted in Section 5.