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  • × author_ss:"Pera, M.S."
  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. Denning, J.; Pera, M.S.; Ng, Y.-K.: ¬A readability level prediction tool for K-12 books (2016) 0.02
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    Abstract
    The readability levels of books identify suitable reading materials. Unfortunately, the majority of published books are assigned a readability level range, which is not useful to readers who look for books at a particular grade level. Existing readability formulas/analysis tools require at least an excerpt of a book to estimate its readability level, which is a severe constraint, since copyright laws prohibit book contents from being made publicly accessible. To alleviate the constraint, we have developed TRoLL which relies on publicly accessible online book metadata, in addition to using a book's snippet, if it is available, to predict its readability level. Based on a multi-dimensional regression analysis, TRoLL determines the grade level of any book instantly, even without a sample of its text, and considers its topical suitability, which is unique. Furthermore, TRoLL is a significant contribution to the educational community, since its computed book readability levels can enrich K-12 readers' book selections and aid parents, teachers, and librarians in locating reading materials suitable for their K-12 readers, which can be a time-consuming and frustrating task that does not always yield a quality outcome. Conducted empirical studies have verified the prediction accuracy of TRoLL and demonstrated its superiority over well-known readability formulas/analysis tools.
  3. Pera, M.S.; Ng, Y.-K.: SpamED : a spam E-mail detection approach based on phrase similarity (2009) 0.01
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  4. Pera, M.S.; Lund, W.; Ng, Y.-K.: ¬A sophisticated library search strategy using folksonomies and similarity matching (2009) 0.01
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