38 research outputs found

    AN INTRODUCTION TO PASCAL’S TRIANGLE

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    Cultural and Linguistic Obstacles for ELLs

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    Abstract This qualitative study aims to assess which aspects of learning a second language English language, learners find challenging and to uncover the role that culture plays in learning a second languages as observed by the students themselves. Students were asked to make note of how cultural differences, assumptions, and biases have either enhanced or hindered the teaching and learning process. It was found that students experienced a sort of indirect marginalization or cultural ‚Äúothering‚ÄĚ. Integration into the mainstream population of society, of the community, or even of the school was described as a major challenge for this particular group of students. Consequently, students had much difficulty achieving a comprehensive understanding of the language and surrounding culture

    ‚ÄúThis I Believe‚ÄĚ About the Teaching of Writing: Secondary Teachers‚Äô Digital Essays About Their Pedagogical Understandings

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    This case study (Merriam & Tisdell, 2016) examines the final projects of two secondary teachers in a graduate course about writing pedagogy. Teachers created digital essays along the lines of the National Public Radio‚Äôs ‚ÄúThis I Believe‚ÄĚ essays, which articulated their beliefs about the teaching of writing. We posed two research questions: a) What pedagogical understandings do teachers identify as their beliefs about writing and how do they represent those ideas in a digital composition? b) What did teachers learn from participating in the process of composing a digital essay? We found that teachers ‚Äúreimagined‚ÄĚ the teaching of writing, were personally drawn to the assignments in ways that surprised them, and realized the power of digital tools to accomplish what words simply cannot fully capture

    Solving Consumer Problems.

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    Detecting Human Features in Summaries- Symbol Sequence Statistical Regularity

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    Abstract. The presented work studies textual summaries, aiming to detect the qualities of human multi-document summaries, in contrast to automatically extracted ones. The measured features are based on a generic statistical regularity measure, named Symbol Sequence Statistical Regularity (SSSR). The measure is calculated over both character and word n-grams of various ranks, given a set of human and automatically extracted multi-document summaries from two different corpora. The results of the experiments indicate that the proposed measure provides enough distinctive power to discriminate between the human and non-human summaries. The results hint on the qualities a human summary holds, increasing intuition related to how a good summary should be generated.
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