4 research outputs found

    A qualitative assessment of preservice elementary teachers\u27 formative perceptions regarding engineering and K-12 engineering education

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    Current teacher education programs provide limited instruction for preservice elementary teachers regarding the incorporation or teaching of engineering concepts and skills in their classrooms. Few studies have been conducted that focus specifically on preservice elementary teachers\u27 formative perceptions and receptivity towards engineering education. That is, not enough is known about what preservice teachers know and think about engineering. The purpose of this qualitative research study was to investigate how forty-four preservice elementary teachers\u27 from a large Midwestern university approached engineering design, the perceptions of engineering and K-12 engineering education that they possessed, and their level of receptiveness with regards to K-12 engineering education. Data were collected using a demographic survey, journal entries, observations, and focus group discussions. The written, verbal, and visual data collected in this study were analyzed using conventional qualitative content analysis, which consisted of inductively developing categories and codes after repeatedly examining the data. The results of the study indicate that the preservice elementary teachers did not utilize any deliberate design process when engaged in a design task. Engineering was perceived as being synonymous with construction and that engineering design consists of trial and error. Participants envisioned their students succeeding in engineering due to their students\u27 prior knowledge, not necessarily the actions of themselves as the teacher. With regards to receptivity, participants expressed apprehension and optimism along with fear and pessimism. Tangential factors also impacted the receptivity of participants

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Whole-Exome Sequencing Identifies Rare and Low-Frequency Coding Variants Associated with LDL Cholesterol

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