Search (157 results, page 1 of 8)

  • × year_i:[2020 TO 2030}
  • × language_ss:"e"
  1. Ekstrand, M.D.; Wright, K.L.; Pera, M.S.: Enhancing classroom instruction with online news (2020) 0.03 
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    Abstract
    Purpose This paper investigates how school teachers look for informational texts for their classrooms. Access to current, varied and authentic informational texts improves learning outcomes for K-12 students, but many teachers lack resources to expand and update readings. The Web offers freely available resources, but finding suitable ones is time-consuming. This research lays the groundwork for building tools to ease that burden. Design/methodology/approach This paper reports qualitative findings from a study in two stages: (1) a set of semistructured interviews, based on the critical incident technique, eliciting teachers' information-seeking practices and challenges; and (2) observations of teachers using a prototype teaching-oriented news search tool under a think-aloud protocol. Findings Teachers articulated different objectives and ways of using readings in their classrooms, goals and self-reported practices varied by experience level. Teachers struggled to formulate queries that are likely to return readings on specific course topics, instead searching directly for abstract topics. Experience differences did not translate into observable differences in search skill or success in the lab study. Originality/value There is limited work on teachers' information-seeking practices, particularly on how teachers look for texts for classroom use. This paper describes how teachers look for information in this context, setting the stage for future development and research on how to support this use case. Understanding and supporting teachers looking for information is a rich area for future research, due to the complexity of the information need and the fact that teachers are not looking for information for themselves.
    Date
    20. 1.2015 18:30:22
  2. Zhang, L.; Lu, W.; Yang, J.: LAGOS-AND : a large gold standard dataset for scholarly author name disambiguation (2023) 0.03 
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    Abstract
    In this article, we present a method to automatically build large labeled datasets for the author ambiguity problem in the academic world by leveraging the authoritative academic resources, ORCID and DOI. Using the method, we built LAGOS-AND, two large, gold-standard sub-datasets for author name disambiguation (AND), of which LAGOS-AND-BLOCK is created for clustering-based AND research and LAGOS-AND-PAIRWISE is created for classification-based AND research. Our LAGOS-AND datasets are substantially different from the existing ones. The initial versions of the datasets (v1.0, released in February 2021) include 7.5 M citations authored by 798 K unique authors (LAGOS-AND-BLOCK) and close to 1 M instances (LAGOS-AND-PAIRWISE). And both datasets show close similarities to the whole Microsoft Academic Graph (MAG) across validations of six facets. In building the datasets, we reveal the variation degrees of last names in three literature databases, PubMed, MAG, and Semantic Scholar, by comparing author names hosted to the authors' official last names shown on the ORCID pages. Furthermore, we evaluate several baseline disambiguation methods as well as the MAG's author IDs system on our datasets, and the evaluation helps identify several interesting findings. We hope the datasets and findings will bring new insights for future studies. The code and datasets are publicly available.
    Date
    22. 1.2023 18:40:36
  3. Thelwall, M.; Kousha, K.; Abdoli, M.; Stuart, E.; Makita, M.; Wilson, P.; Levitt, J.: Why are coauthored academic articles more cited : higher quality or larger audience? (2023) 0.03 
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    Date
    22. 6.2023 18:11:50
  4. Vakkari, P.; Järvelin, K.; Chang, Y.-W.: ¬The association of disciplinary background with the evolution of topics and methods in Library and Information Science research 1995-2015 (2023) 0.03 
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    Date
    22. 6.2023 18:15:06
  5. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.03 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  6. Hoffman, G.L.; Snow, K.: Cataloging and classification : back to basics: introduction (2021) 0.02 
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  7. Roose, K.: ¬The brilliance and weirdness of ChatGPT (2022) 0.02 
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  8. Chessum, K.; Haiming, L.; Frommholz, I.: ¬A study of search user interface design based on Hofstede's six cultural dimensions (2022) 0.02 
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  9. Reynolds, R.; Aromi, J.; McGowan, C.; Paris, B.: Digital divide, critical-, and crisis-informatics perspectives on K-12 emergency remote teaching during the pandemic (2022) 0.02 
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    Abstract
    The rapid deployment of emergency remote teaching in the pandemic presents sweeping societal-level information systems phenomena worthy of scholarly inquiry. This paper reports findings from teacher interviews conducted with K-12 public school teachers, exploring how digital access and use gaps in communities reflect wider digital and social inequalities as schools fulfilled emergency remote teaching mandates, becoming swept up into e-learning technology expansion trends propelled by mandates, and unfettered corporate edtech. Results show persistence of home and school level digital affordance gaps as hindrances to pandemic pedagogy. We build upon theory of the digital divide, and crisis and critical informatics literature considering how critical approaches to the study of socio-technical systems research can inform these understandings, providing insights into how localized digital inequities contribute to broader digital inequality and social inequality, in the educative processes expected of public education in democratic societies. Our work gives voice to one highly pressured and conflicted stakeholder in these dynamics-K-12 public school teachers-and demonstrates some of the ways in which digital inequity gaps may play a further magnifying role of societal division through expanding edtech deployment in K-12 grades, if current edtech trends hold.
  10. Fugmann, R.: What is information? : an information veteran looks back (2022) 0.02 
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    Date
    18. 8.2022 19:22:57
  11. Danskin, A.; Seeman, D.; Bouchard, M.; Kammerer, K.; Kilpatrick, L.; Mumbower, K.: FAST the inside track : where we are, where do we want to be, and how do we get there? (2023) 0.01 
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  12. Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024) 0.01 
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    Abstract
    Detecting science-technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.
  13. Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020) 0.01 
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  14. Gödert, W.; Lepsky, K.: Information literacy and autonomy : a cognitive view (2023) 0.01 
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  15. Morris, V.: Automated language identification of bibliographic resources (2020) 0.01 
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    Date
    2. 3.2020 19:04:22
  16. Andrushchenko, M.; Sandberg, K.; Turunen, R.; Marjanen, J.; Hatavara, M.; Kurunmäki, J.; Nummenmaa, T.; Hyvärinen, M.; Teräs, K.; Peltonen, J.; Nummenmaa, J.: Using parsed and annotated corpora to analyze parliamentarians' talk in Finland (2022) 0.01 
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  17. Lindau, S.T.; Makelarski, J.A.; Abramsohn, E.M.; Beiser, D.G.; Boyd, K.; Huang, E.S.; Paradise, K.; Tung, E.L.: Sharing information about health-related resources : observations from a community resource referral intervention trial in a predominantly African American/Black community (2022) 0.01 
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  18. Verma, N.; Fleischmann, K.R.; Zhou, L.; Xie, B.; Lee, M.K.; Rich, K.; Shiroma, K.; Jia, C.; Zimmerman, T.: Trust in COVID-19 public health information (2022) 0.01 
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  19. Han, K.; Rezapour, R.; Nakamura, K.; Devkota, D.; Miller, D.C.; Diesner, J.: ¬An expert-in-the-loop method for domain-specific document categorization based on small training data (2023) 0.01 
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