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Restrepo, Silvia; ter Horst, Enrique; Zambrano, Juan Diego; Gunn, Laura H.; Molina, German; Salazar, Carlos Andres – Education for Information, 2022
This manuscript builds on a novel, automatic, freely-available Bayesian approach to extract information in abstracts and titles to classify research topics by quartile. This approach is demonstrated for all N= 149,129 ISI-indexed publications in biological sciences journals during 2017. A Bayesian multinomial inverse regression approach is used to…
Descriptors: Bayesian Statistics, Biological Sciences, Trend Analysis, Classification
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Winchell, Adam; Mozer, Michael; Lan, Andrew; Grimaldi, Phillip; Pashler, Harold – International Educational Data Mining Society, 2018
When engaging with a textbook, students are inclined to highlight key content. Although students believe that highlighting and subsequent review of the highlights will further their educational goals, the psychological literature provides no evidence of benefits. Nonetheless, a student's choice of text for highlighting may serve as a window into…
Descriptors: Textbooks, Biology, Documentation, Science Instruction
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Parish, Jesse; Parks, Rodney; Fryer, Jack – College and University, 2017
This study discusses innovations in digital credentialing, namely the Elon Visual EXP, and the broader implications of documenting student experiences in co-curricular, high-impact practices. Following a similar survey of employer observations of the new transcript, a survey was sent to all undergraduates of Elon University to assess their overall…
Descriptors: Academic Records, Student Attitudes, Student Records, Credentials
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White, Lee J.; And Others – 1975
The major advantage of sequential classification, a technique for automatically classifying documents into previously selected categories, is that the entire document need not be processed before it is classified. This method assumes the availability of a priori categories, a selection of keywords representative of these categories, and the a…
Descriptors: Algorithms, Automatic Indexing, Bayesian Statistics, Classification
Kar, B. Gautam; White, Lee J. – 1975
The feasibility of using a distance measure, called the Bayesian distance, for automatic sequential document classification was studied. Results indicate that, by observing the variation of this distance measure as keywords are extracted sequentially from a document, the occurrence of noisy keywords may be detected. This property of the distance…
Descriptors: Algorithms, Automatic Indexing, Bayesian Statistics, Classification