Search (5 results, page 1 of 1)

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Abstract

This review introduces a comprehensive model of the cognitive process and mechanisms of individual sensemaking to provide a theoretical basis for: - empirical studies that improve our understanding of the cognitive process and mechanisms of sensemaking and integration of results of such studies; - education in critical thinking and sensemaking skills; - the design of sensemaking assistant tools that support and guide users. The paper reviews and extends existing sensemaking models with ideas from learning and cognition. It reviews literature on sensemaking models in human-computer interaction (HCI), cognitive system engineering, organizational communication, and library and information sciences (LIS), learning theories, cognitive psychology, and task-based information seeking. The model resulting from this synthesis moves to a stronger basis for explaining sensemaking behaviors and conceptual changes. The model illustrates the iterative processes of sensemaking, extends existing models that focus on activities by integrating cognitive mechanisms and the creation of instantiated structure elements of knowledge, and different types of conceptual change to show a complete picture of the cognitive processes of sensemaking. The processes and cognitive mechanisms identified provide better foundations for knowledge creation, organization, and sharing practices and a stronger basis for design of sensemaking assistant systems and tools.

Date

22. 8.2014 16:55:39

Type

a

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Abstract

As the information field (IField) becomes more recognized by different constituencies for education and research, the need to better understand its intellectual characteristics becomes more compelling. Although there are various conceptualizations of the IField, to date, in-depth studies based on empirical evidence are scarce. This article reports a study that fills this gap. We focus on the first five ISchools in the ICaucus as a proxy to represent the IField. The intellectual characteristics are depicted by two independent sets of data on tenure track faculty as knowledge contributors: their intellectual heritages and the intellectual substance in their journal publications. We use a critical analysis method to examine doctoral training areas and 3 years of journal publications. Our results indicate that (a) the IField can be better conceptualized with empirical support by a four-component model that includes People, Information, Technology, and Management, as predicted by the I-Model (Zhang & Benjamin, 2007); (b) the ISchools' faculty members are diverse, interdisciplinary, and multidisciplinary as shown by their intellectual heritages, by their research foci, by journals in which they publish, by the contexts within which they conduct research, and by the levels of analysis in research investigations; (c) the five ISchools share similarities while evincing differences in both faculty heritages and intellectual substances; (d) ISchool tenure track faculty members do not collaborate much with each other within or across schools although there is great potential; and (e) intellectual heritages are not good predictors of scholars' intellectual substance. We conclude by discussing the implications of the findings on IField identity, IField development, new ISchool formation and existing ISchool evolution, faculty career development, and collaboration within the IField.

Type

a

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Abstract

Modeling multidimensional relevance in information retrieval (IR) has attracted much attention in recent years. However, most existing studies are conducted through relatively small-scale user studies, which may not reflect a real-world and natural search scenario. In this article, we propose to study the multidimensional user relevance model (MURM) on large scale query logs, which record users' various search behaviors (e.g., query reformulations, clicks and dwelling time, etc.) in natural search settings. We advance an existing MURM model (including five dimensions: topicality, novelty, reliability, understandability, and scope) by providing two additional dimensions, that is, interest and habit. The two new dimensions represent personalized relevance judgment on retrieved documents. Further, for each dimension in the enriched MURM model, a set of computable features are formulated. By conducting extensive document ranking experiments on Bing's query logs and TREC session Track data, we systematically investigated the impact of each dimension on retrieval performance and gained a series of insightful findings which may bring benefits for the design of future IR systems.

Type

a

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Abstract

This study compares the 45 "Best Paper" award articles with nonaward articles published in the Journal of Association for Information Science and Technology (JASIST) to observe the differences in citations. The results show that, in most cases, the citations of the award articles are more numerous than the median, belonging to the Top-50% stratum. Only 15.6% of the award articles have the status of being the most-cited article of the year in which the article was published; 24.4% belong to the Top-5% stratum of the publication year; 44.4% belong to the Top-10% stratum of the publication year; and 73.3% belong to the Top-25% stratum of the publication year. Surprisingly, from 2000 to 2012, none of the award articles made it to the Top-10% stratum, apart from the year 2004; the least-cited award article received only three citations during a 5-year period. The results show a wide range of citations among the Best JASIST Papers. This study also observes that the number of articles changed little from 1969 to 1995 but grew rapidly from 1996 to 2012. Suggestions for possible ways to better meet the challenges of the journal's growth in size and scope in selecting award articles are provided.

Type

a

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Type

a