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ALTUN, MURAT

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ALTUN

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MURAT

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2016 - 201912020 - 20221
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    Publication
    Analyzing in-service teachers' process of mathematical literacy problem posing
    (Modestum Ltd, 2022-08-01) Bozkurt, Işıl; Ülger, Tuğçe Kozaklı; KOZAKLI ÜLGER, TUĞÇE; Altun, Murat; ALTUN, MURAT; Bursa Uludağ Üniversitesi/Eğitim Fakültesi.; 0000-0001-8413-8290; A-6621-2018
    Posing mathematical literacy problems is one of the challenging issues in literacy studies. The present study focused on how to develop the ability to pose mathematical literacy problems. Within the context of the training given to middle school mathematics teachers on how to pose mathematical literacy problems, literacy problems were introduced. Each of the problems was discussed and evaluated regarding the structural characteristics and differences from the routine problems. Respectively, the steps of (i) completing an incomplete problem with a context as a mathematical literacy problem; (ii) evaluating the problems in the textbooks in terms of realistic problems and converting them into mathematical literacy problems; and (iii) conducting exercises for writing original mathematical literacy problems. It was concluded that teachers were more successful in creating contexts, that they tended to write operation-oriented problems, that they often used sentences ending with the words "please calculate" and "find" by being influenced by the traditional word-problem sentences contained in the textbooks, and that they had more difficulty in developing problems which should bring forward a mathematical suggestion than other problem types.
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    Comparison of classical linear regression and orthogonal regression with respect to the sum of squared perpendicular distances
    (Assoc Measurement & Evaluation Education & Psychology, 2016-12-01) Keleş, Taliha; Altun, Murat; ALTUN, MURAT; Bursa Uludağ Üniversitesi/Eğitim Fakültesi.
    Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it was seen that OR line has appeared to present a much better fit for the data than CLR line. Depending on those results, the OR is thought to be a regression technique to obtain more accurate results than CLR at simple linear regression studies.