Catherine L. Smith

Bio: Catherine L. Smith is an academic researcher from Kent State University. The author has contributed to research in topics: Human–computer information retrieval & Web query classification. The author has an hindex of 8, co-authored 27 publications receiving 227 citations. Previous affiliations of Catherine L. Smith include Rutgers University.

User adaptation: good results from poor systems

Abstract: Several recent studies have found only a weak relationship between the performance of a retrieval system and the "success" achievable by human searchers. We hypothesize that searchers are successful precisely because they alter their behavior. To explore the possible causal relation between system performance and search behavior, we control system performance, hoping to elicit adaptive search behaviors. 36 subjects each completed 12 searches using either a standard system or one of two degraded systems. Using a general linear model, we isolate the main effect of system performance, by measuring and removing main effects due to searcher variation, topic difficulty, and the position of each search in the time series. We find that searchers using our degraded systems are as successful as those using the standard system, but that, in achieving this success, they alter their behavior in ways that could be measured, in real time, by a suitably instrumented system. Our findings suggest, quite generally, that some aspects of behavioral dynamics may provide unobtrusive indicators of system performance.

Knowledge-Context in Search Systems: Toward Information-Literate Actions

Abstract: In this perspectives paper we define knowledge-context as meta information that searchers use when making sense of information displayed in and accessible from a search engine results page (SERP). We argue that enriching the knowledge-context in SERPs has great potential for facilitating human learning, critical thinking, and creativity by expanding searchers' information-literate actions such as comparing, evaluating, and differentiating between information sources. Thus it supports the development of learning-centric search systems. Using theories and empirical findings from psychology and the learning sciences, we first discuss general effects of Web search on memory and learning. After reviewing selected research addressing metacognition and self-regulated learning, we discuss design goals for search systems that support metacognitive skills required for long-term learning, creativity, and critical thinking. We then propose that SERPs make both bibliographic and inferential knowledge-context readily accessible to motivate and facilitate information-literate actions for learning and creative endeavors. A brief discussion of related ideas, designs, and prototypes found in prior work follows. We conclude the paper by presenting future research directions and questions on knowledge-context, information-literate actions, and learning-centric search systems.

The use of query auto-completion over the course of search sessions with multifaceted information needs

Abstract: Query auto-completion (QAC) is the ubiquitous information search function that displays a list of suggested queries, where the list changes as the searcher types. This article reports on an exploratory study of QAC usage during complete search sessions in a lab study of 29 participants, where a session comprised searching on an assigned multi-faceted task. While prior research has reported average usage rates independent of the structure of search sessions, our findings are the first to describe when QAC was used within whole sessions and associations between usage and search task subtopics. Results show the value of QAC in shorter sessions and higher retrieval performance. Importantly, results also show that when QAC was used, it was most likely for the first query of a session, that use was less likely for subsequent queries, and that when the first query of a session did not use QAC, subsequent use was far less likely. The findings have implications for the value of QAC models that use knowledge from prior queries within a session to converge on optimal suggestions over successive queries. The findings are important for development of useful query assistance mechanisms for searchers. The study leads to new research questions on the effect of reformulation patterns on QAC usefulness and searchers’ attention to QAC throughout search sessions.

Rutgers' HARD Track Experiences at TREC 2004

Abstract: The goal of our work in the HARD track was to test techniques for using knowledge about various aspects of the information seeker’s context to improve IR system performance. We were particularly concerned with such knowledge which could be gained through implicit sources of evidence, rather than explicit questioning of the information seeker. We therefore did not submit any clarification form 1 , preferring to rely on the categories of supplied metadata concerning the user which we believed could, at least in principle, be inferred from user behavior, either in the past or during the current information seeking episode. The experimental condition of the HARD track was for each site to submit at least one baseline run for the set of 50 topics, using only the title and (optionally) description fields for query construction. The results of the baseline run(s) were compared with the results from one or more experimental runs, which made use of the supplied searcher metadata, and of a clarification form submitted to the searcher, asking for whatever information each site thought would be useful in improving search results. We used only the supplied metadata, for the reasons stated above, and especially because we were interested in how to make initial queries better, rather than in how to conduct a dialogue with a searcher. There were five categories of searcher metadata for each topic (not all topics had values for all five): Genre, Familiarity, Geography, Granularity and Related text(s), which were intended to represent aspects of the searcher’s context which might be useful in tailoring retrieval to the individual, and the individual situation. We made the assumption that at least some of these categories would be available to the IR system prior to (or in conjunction with) the specific search session, either through explicit or implicit evidence. Therefore, for us the HARD track experimental condition was designed to test whether knowledge of these contextual characteristics, and our specific ways of using that knowledge, would result in better retrieval performance than a good IR system without such knowledge. We understood that there would be, in general, two ways in which to take account of the metadata. One would be to modify the initial query from the (presumed) searcher, before submitting it for search; the other would be to search with the initial query, and then to modify (i.e. re-rank) the results before showing them to the searcher. We used both, but mainly concentrated on the latter of these techniques in taking account of the different types of metadata. 2 Hypotheses for How to Take Account of Metadata Categories and Values Our general approach was to generate hypotheses about how to take account of each of the categories of metadata information in order to improve retrieval effectiveness, to operationalize them in the TREC HARD setting, to test each hypothesis individually on the training corpus, and then to combine the best-performing ones from each category to generate a final result list for the test corpus. Below are summarized the various improvement methods used based on metadata: Table 1. Methods for improving retrieval effectiveness based on metadata.

Does Familiarity Breed Content? Taking Account of Familiarity with a Topic in Personalizing Information Retrieval

Abstract: We report on an evaluation of the effectiveness of considering a user's familiarity with a topic in improving information retrieval performance. This approach to personalization is based on previous results indicating differences in user search behavior and judgments according to his/her familiarity to the topic explored, and to research on using implicit sources of evidence to determine the user's context and preferences. Our attempt was to relate a topic-dependent concept and measure, familiarity with the topic, with topic-independent measures of documents such as readability, concreteness/abstractness, and specificity/generality. Contrary to our expectations, a user’s familiarity with a topic has no effect on the utility of readability or concrete/abstract scoring. We are encouraged, however, to find that high readability had a positive effect on search results, regardless of a user’s familiarity with a topic.

Annual Report Readability, Current Earnings, and Earnings Persistence

Abstract: This paper examines the relationship between annual report readability and firm performance and earnings persistence. This is motivated by the Securities and Exchange Commission's plain English disclosure regulations that attempt to make corporate disclosures easier to read for ordinary investors. I measure the readability of public company annual reports using both the Fog Index from computational linguistics and the length of the document. I find that the annual reports of firms with lower earnings are harder to read (i.e., they have higher Fog and are longer). Moreover, the positive earnings of firms with annual reports that are easier to read are more persistent. This suggests that managers may be opportunistically choosing the readability of annual reports to hide adverse information from investors.

Annual report readability, current earnings, and earnings persistence

Abstract: This paper examines the relation between annual report readability and firm performance and earnings persistence. I measure the readability of public company annual reports using the Fog index from the computational linguistics literature and the length of the document. I find that: (1) the annual reports of firms with lower earnings are harder to read (i.e., they have a higher Fog index and are longer); and (2) firms with annual reports that are easier to read have more persistent positive earnings.

The Cambridge Handbook of Expertise and Expert Performance

Abstract: This is the first handbook where the world’s foremost “experts on expertise” review our scientific knowledge on expertise and expert performance and how experts may differ from non-experts in terms of their development, training, reasoning, knowledge, social support, and innate talent. Methods are described for the study of experts’ knowledge and their performance of representative tasks from their domain of expertise. The development of expertise is also studied by retrospective interviews and the daily lives of experts are studied with diaries. In 15 major domains of expertise, the leading researchers summarize our knowledge of the structure and acquisition of expert skill and knowledge and discuss future prospects. General issues that cut across most domains are reviewed in chapters on various aspects of expertise, such as general and practical intelligence, differences in brain activity, self-regulated learning, deliberate practice, aging, knowledge management, and creativity.

The Cambridge Handbook Of The Learning Sciences

Abstract: the cambridge handbook of the learning sciences is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the the cambridge handbook of the learning sciences is universally compatible with any devices to read.

Methods for Evaluating Interactive Information Retrieval Systems with Users

Abstract: This paper provides overview and instruction regarding the evaluation of interactive information retrieval systems with users The primary goal of this article is to catalog and compile material related to this topic into a single source This article (1) provides historical background on the development of user-centered approaches to the evaluation of interactive information retrieval systems; (2) describes the major components of interactive information retrieval system evaluation; (3) describes different experimental designs and sampling strategies; (4) presents core instruments and data collection techniques and measures; (5) explains basic data analysis techniques; and (4) reviews and discusses previous studies This article also discusses validity and reliability issues with respect to both measures and methods, presents background information on research ethics and discusses some ethical issues which are specific to studies of interactive information retrieval (IIR) Finally, this article concludes with a discussion of outstanding challenges and future research directions