Knowledge-Context in search systems: Toward information-literate actions

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.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148270/1/Smith and Rieh Knowledge-Context in Search Systems CHIIR2019.pd

Una teoría cognitiva integral para la recuperación de información: saliendo del entorno del laboratorio

The paper demonstrates how the Laboratory Research Framework fits into the integrated Cognitive Framework for IR. It first discusses the Laboratory Framework with emphasis on its underlying assumptions and known limitations. This is followed by a view of interaction and relevance phenomena associated with IR evaluation and central to the understanding of IR. The ensuing section outlines how interactive IR is viewed from a Cognitive Framework, and ‘light’ interactive IR experiments are suggested performed by drawing on the latter framework’s contextual possibilities. These include independent variables drawn from a collection, matching principles in a retrieval system, and the searcher’s situation and task context. The paper ends with concluding points of summarization of issues encountered.Este artículo demuestra cómo el marco de investigación en laboratorio encaja bien dentro del marco cognitivo integral para la Recuperación de información. Se discute primero el marco de investigación en laboratorio, con énfasis en sus asunciones y limitaciones. Se analizan los fenómenos de la interacción y relevancia asociados con la evaluación en RI., así como el modo de desarrollar experimentos interactivos de Recuperación de información dentro del marco cognitivo, considerando la situación del investigador y el contexto de la tarea llevada a cabo

Youth and Digital Media: From Credibility to Information Quality

Building upon a process-and context-oriented information quality framework, this paper seeks to map and explore what we know about the ways in which young users of age 18 and under search for information online, how they evaluate information, and how their related practices of content creation, levels of new literacies, general digital media usage, and social patterns affect these activities. A review of selected literature at the intersection of digital media, youth, and information quality -- primarily works from library and information science, sociology, education, and selected ethnographic studies -- reveals patterns in youth's information-seeking behavior, but also highlights the importance of contextual and demographic factors both for search and evaluation. Looking at the phenomenon from an information-learning and educational perspective, the literature shows that youth develop competencies for personal goals that sometimes do not transfer to school, and are sometimes not appropriate for school. Thus far, educational initiatives to educate youth about search, evaluation, or creation have depended greatly on the local circumstances for their success or failure

Journalistic image access : description, categorization and searching

The quantity of digital imagery continues to grow, creating a pressing need to develop efficient methods for organizing and retrieving images. Knowledge on user behavior in image description and search is required for creating effective and satisfying searching experiences. The nature of visual information and journalistic images creates challenges in representing and matching images with user needs. The goal of this dissertation was to understand the processes in journalistic image access (description, categorization, and searching), and the effects of contextual factors on preferred access points. These were studied using multiple data collection and analysis methods across several studies. Image attributes used to describe journalistic imagery were analyzed based on description tasks and compared to a typology developed through a meta-analysis of literature on image attributes. Journalistic image search processes and query types were analyzed through a field study and multimodal image retrieval experiment. Image categorization was studied via sorting experiments leading to a categorization model. Advances to research methods concerning search tasks and categorization procedures were implemented. Contextual effects on image access were found related to organizational contexts, work, and search tasks, as well as publication context. Image retrieval in a journalistic work context was contextual at the level of image needs and search process. While text queries, together with browsing, remained the key access mode to journalistic imagery, participants also used visual access modes in the experiment, constructing multimodal queries. Assigned search task type and searcher expertise had an effect on query modes utilized. Journalistic images were mostly described and queried for on the semantic level but also syntactic attributes were used. Constraining the description led to more abstract descriptions. Image similarity was evaluated mainly based on generic semantics. However, functionally oriented categories were also constructed, especially by domain experts. Availability of page context promoted thematic rather than object-based categorization. The findings increase our understanding of user behavior in image description, categorization, and searching, as well as have implications for future solutions in journalistic image access. The contexts of image production, use, and search merit more interest in research as these could be leveraged for supporting annotation and retrieval. Multiple access points should be created for journalistic images based on image content and function. Support for multimodal query formulation should also be offered. The contributions of this dissertation may be used to create evaluation criteria for journalistic image access systems

Navigating Complex Search Tasks with AI Copilots

As many of us in the information retrieval (IR) research community know and appreciate, search is far from being a solved problem. Millions of people struggle with tasks on search engines every day. Often, their struggles relate to the intrinsic complexity of their task and the failure of search systems to fully understand the task and serve relevant results. The task motivates the search, creating the gap/problematic situation that searchers attempt to bridge/resolve and drives search behavior as they work through different task facets. Complex search tasks require more than support for rudimentary fact finding or re-finding. Research on methods to support complex tasks includes work on generating query and website suggestions, personalizing and contextualizing search, and developing new search experiences, including those that span time and space. The recent emergence of generative artificial intelligence (AI) and the arrival of assistive agents, or copilots, based on this technology, has the potential to offer further assistance to searchers, especially those engaged in complex tasks. There are profound implications from these advances for the design of intelligent systems and for the future of search itself. This article, based on a keynote by the author at the 2023 ACM SIGIR Conference, explores these issues and charts a course toward new horizons in information access guided by AI copilots.Comment: 10 pages, 6 figure

Investigating User Search Tactic Patterns and System Support in Using Digital Libraries

This study aims to investigate users\u27 search tactic application and system support in using digital libraries. A user study was conducted with sixty digital library users. The study was designed to answer three research questions: 1) How do users engage in a search process by applying different types of search tactics while conducting different search tasks?; 2) How does the system support users to apply different types of search tactics?; 3) How do users\u27 search tactic application and system support for different types of search tactics affect search outputs? Sixty student subjects were recruited from different disciplines in a state research university. Multiple methods were employed to collect data, including questionnaires, transaction logs and think-aloud protocols. Subjects were asked to conduct three different types of search tasks, namely, known-item search, specific information search and exploratory search, using Library of Congress Digital Libraries. To explore users\u27 search tactic patterns (RQ1), quantitative analysis was conducted, including descriptive statistics, kernel regression, transition analysis, and clustering analysis. Types of system support were explored by analyzing system features for search tactic application. In addition, users\u27 perceived system support, difficulty, and satisfaction with search tactic application were measured using post-search questionnaires (RQ2). Finally, the study examined the causal relationships between search process and search outputs (RQ 3) based on multiple regression and structural equation modeling. This study uncovers unique behavior of users\u27 search tactic application and corresponding system support in the context of digital libraries. First, search tactic selections, changes, and transitions were explored in different task situations - known-item search, specific information search, and exploratory search. Search tactic application patterns differed by task type. In known-item search tasks, users preferred to apply search query creation and following search result evaluation tactics, but less query reformulation or iterative tactic loops were observed. In specific information search tasks, iterative search result evaluation strategies were dominantly used. In exploratory tasks, browsing tactics were frequently selected as well as search result evaluation tactics. Second, this study identified different types of system support for search tactic application. System support, difficulty, and satisfaction were measure in terms of search tactic application focusing on search process. Users perceived relatively high system support for accessing and browsing tactics while less support for query reformulation and item evaluation tactics. Third, the effects of search tactic selections and system support on search outputs were examined based on multiple regression. In known-item searches, frequencies of query creation and accessing forwarding tactics would positively affect search efficiency. In specific information searches, time spent on applying search result evaluation tactics would have a positive impact on success rate. In exploratory searches, browsing tactics turned out to be positively associated with aspectual recall and satisfaction with search results. Based on the findings, the author discussed unique patterns of users\u27 search tactic application as well as system design implications in digital library environments

Towards searching as a learning process: A review of current perspectives and future directions

We critically review literature on the association between searching and learning and contribute to the formulation of a research agenda for searching as learning. The paper begins by reviewing current literature that tends to characterize search systems as tools for learning. We then present a perspective on searching as learning that focuses on the learning that occurs during the search pro-cess, as well as search outputs and learning outcomes. The concept of ‘comprehensive search’ is proposed to describe iterative, reflec-tive and integrative search sessions that facilitate critical and creative learning beyond receptive learning. We also discuss how search interaction data can provide a rich source of implicit and explicit features through which to assess search-related learning. In conclu-sion, we summarize opportunities and challenges for future research with respect to four agendas: developing a search system that supports sense-making and enhances learning; supporting effective user interaction for searching as learning; providing an inquiry-based literacy tool within a search system; and assessing learning from online searching behaviour.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145734/1/Rieh et al Towards searching as a learning process JIS2016.pd