Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, Sayı: 63, 311 - 332, 31.07.2022
https://doi.org/10.21764/maeuefd.1037681

Öz

Kaynakça

  • Ahmed, A. B. ve Elaraby, I. S. (2014). Data mining: A prediction for student's performance using classification method. World Journal of Computer Application and Technology, 2(2), 43-47.
  • Altunkaynak, A. (2009). Sediment load prediction by genetic algorithms. Advances in Engineering Software, 40(9), 928-934.
  • Anderson, R. C., Wilson, P. T. ve Fielding, L. G. (1988). Growth in reading and how children spend their time outside of school. Reading Research Quarterly, 285-303.
  • Arnett, A. B., Pennington, B. F., Peterson, R. L., Willcutt, E. G., DeFries, J. C. ve Olson, R. K. (2017). Explaining the sex difference in dyslexia. Journal of Child Psychology and Psychiatry, 58(6), 719–727.
  • Arslan, B., Jiang, Y., Keehner, M., Gong, T., Katz, I. R. ve Yan, F. (2020). The effect of drag‐and‐drop item features on test‐taker performance and response strategies. Educational Measurement: Issues and Practice, 39(2), 96-106.
  • Başaran, M. (2013). Okuduğunu anlamanın bir göstergesi olarak akıcı okuma. Kuram ve Uygulamada Eğitim Bilimleri, 13(4), 2277-2290.
  • Büyüköztürk, Ş., Çakmak, E. K., Akgün, Ö. E., Karadeniz, Ş. ve Demirel, F. (2014). Bilimsel Araştırma Yöntemleri (16. bs.). Ankara: Pegem.
  • Cañizo, M. A., Suárez-Coalla, P. ve Cuetos, F. (2015). The role of reading fluency in children’s text comprehension. Frontiers in psychology, 6, 1810.
  • Dodonova Y. A. ve Dodonov Y. S. (2013). Faster on easy items, more accurate on difficult ones: Cognitive ability and performance on a task of varying difficulty. Intelligence, 41(1), 1-10.
  • Enders, C. K. (2010). Applied Missing Data Analysis. New York: The Guilford Press.
  • Fox, J. P. ve Marianti, S. (2017). Person-fit statistics for joint models for accuracy and speed. Journal of Educational Measurement, 54(2), 243-262.
  • Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Massachusetts: Addison-Wesley.
  • Goldhammer, F., Naumann, J. ve Greiff, S. (2015). More is not always better: The relation between item response and item response time in Raven’s matrices. Journal of Intelligence, 3(1), 21-40.
  • Greiff, S., Niepel, C., Scherer, R. ve Martin, R. (2016). Understanding students’ performance in a computer-based assessment of complex problem solving: An analysis of behavioral data from computer-generated log files. Computers in Human Behavior, 61(2016), 36-46.
  • Gürses, R. (1996). Okuma anlama üzerine. Atatürk Kültür, Dil ve Tarih Yüksek Kurumu Bülteni, 28(9), 98-103.
  • Haupt, R. L. ve Haupt, S. E. (1998). Practical Genetic Algorithms. USA: Willey-Interscience Publication.
  • Kaan, E., Ballantyne, J. C. ve Wijnen, F. (2015). Effects of reading speed on second-language sentence processing. Applied Psycholinguistics, 36(4), 799-830.
  • Kantemir, E. (1995). Yazılı ve Sözlü Anlatım. Ankara: Engin Yayınevi.
  • Karia, R. M., Ghuntla, T. P., Mehta, H. B., Gokhale, P. A. ve Shah, C. J. (2012). Effect of gender difference on visual reaction time: A study on medical students of Bhavnagar region. IOSR Journal of Pharmacy, 2(3), 452-454.
  • Kroehne, U., Hahnel, C. ve Goldhammer, F. (2019). Invariance of the response processes between gender and modes in an assessment of reading. Frontiers in Applied Mathematics and Statistics, 5, 2.
  • Kuhn, M., Wickham, H. ve RStudio. (2021). Preprocessing and Feature Engineering Steps for Modeling. R package version 0.1.17.
  • Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., R Core Team, Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C. ve Hunt, T. (2021). Classification and Regression Training. R package version 6.0-90.
  • Kushchu, I. (2002). Genetic programming and evolutionary generalization. IEEE Transactions on Evolutionary Computation, 6(5), 431-442.
  • Kyllonen, P. ve Zu, J. (2016). Use of response time for measuring cognitive ability. Journal of Intelligence, 4(4), 1-29.
  • Lai, F. K. (1993). The effect of a summer reading course on reading and writing skills. System, 21(1), 87-100.
  • Leardi, R., Boggia, R. ve Terrile, M. (1992). Genetic algorithms as a strategy for feature selection. Journal of Chemometrics, 6(5), 267-281.
  • Lee, Y. H. ve Haberman, S. J. (2016) Investigating test-taking behaviors using timing and process data. International Journal of Testing, 16(3), 240-267.
  • Lee, Y. H. ve Jia, Y. (2014). Using response time to investigate students’ test-taking behaviors in a NAEP computer-based study. Large-scale Assessments in Education, 2(8), 1-24.
  • Leisch, F. ve Dimitriadou, E. (2021). Machine Learning Benchmark Problems. R package version 2.1-3.
  • Marczyk, G., DeMatteo, D. ve Festinger, D. (2005). Essentials of Research Design and Methodology (Vol. 2). New York: John Wiley & Sons.
  • Michaelides, M. P., Ivanova, M. ve Nicolaou, C. (2020). The relationship between response-time effort and accuracy in PISA science multiple choice items. International Journal of Testing, 20(3), 187-205.
  • Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs (3.ed.). USA: Springer.
  • Minghua, S., Qingxian, X., Benda, Z. ve Feng, Y. (2017). Regression modelling based on improved genetic algoritm. Tehnicki vjesnik/Technical Gazette, 24(1).
  • National Research Council (2001). Knowing What Students Know: The Science and Design of educational Assessment. J. Pelligrino, N. Chudowsky, & R. Glaser (Eds.), Washington, DC: National Academy Press.
  • Oakhill, J., Cain, K. ve Elbro, C. (2015). Understanding and Teaching Reading Comprehension: A Handbook. New York: Routledge.
  • OECD. (2017). PISA 2015 Technical Report. Paris: OECD Publishing.
  • Örkcü, H. H. (2009). Ayırma analizine matematiksel programlama ve yapay sinir ağları yaklaşımları. Yayınlanmamış doktora tezi, Gazi Üniversitesi, Ankara.
  • Özdemir, M. (2017). Genetik algoritma ile doğrusal regresyonda tahmin amaçlı model seçimi. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 28, 213-233.
  • Pahmi, S., Saepudin, S., Maesarah, N., Solehudin, U. I. ve Wulandari (2018). Implementation of CART (classification and regression trees) algorithm for determining factors affecting employee performance. In: 2018 International Conference on Computing, Engineering, and Design (ICCED) 6-8 September 2018 (pp. 57-62). Bangkok: Institute of Electrical and Electronics Engineers (IEEE).
  • Paterlini, S. ve Minerva, T. (2010, June). Regression model selection using genetic algorithms. In Proceedings of the 11th WSEAS International Conference on Nural Networks and 11th WSEAS International Conference on Evolutionary Computing and 11th WSEAS International Conference on Fuzzy Systems (pp. 19-27). World Scientific and Engineering Academy and Society (WSEAS).
  • Ponce, H. R., Mayer, R. E., Sitthiworachart, J. ve López, M. J. (2020). Effects on response time and accuracy of technology-enhanced cloze tests: An eye-tracking study. Educational Technology Research and Development, 68(5), 2033-2053.
  • Rios, J. A., Guo, H., Mao, L. ve Liu, O. L. (2017). Evaluating the impact of careless responding on aggregated-scores: To filter unmotivated examinees or not?. International Journal of Testing, 17(1), 74-104.
  • Schnipke, D. L. ve Scrams, D. J. (2002). Exploring issues of examinee behavior: Insights gained from response-time analyses. Computer-Based Testing: Building the Foundation for Future Assessments, 237-266.
  • Scrucca, L. (2021). Genetic Algorithms. R package version 3.2.2.
  • Silge, J., Chow, F., Kuhn, M., Wickham, H. ve RStudio. (2021). General Resampling Infrastructure. R package version 0.1.1.
  • Şen, Z. ve Öztopal, A. (2001). Genetic algorithms for the classification and prediction of precipitation occurrence. Hydrological Sciences Journal, 46(2), 255-267.
  • Taguchi, N. (2005). Comprehending implied meaning in English as a foreign language. The Modern Language Journal, 89(4), 543-562.
  • Tanaka, H. ve Stapleton, P. (2007). Increasing reading input in Japanese high school EFL classrooms: An empirical study exploring the efficacy of extensive reading. The Reading Matrix, 7(1).
  • Temizkan, M. (2007). İlköğretim ikinci kademe Türkçe derslerinde okuma stratejilerinin okuduğunu anlama üzerindeki etkisi. Yayımlanmamış doktora tezi, Gazi Üniversitesi, Ankara.
  • Tolvi, J. (2004). Genetic algorithms for outlier detection and variable selection in linear regression models. Soft Computing, 8(8), 527-533.
  • Trejos, J., Villalobos-Arias, M. A. ve Espinoza, J. L. (2016). Variable selection in multiple linear regression using a genetic algorithm. In Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics (pp. 133-159). IGI Global.
  • van Bergen, E., van Zuijen, T., Bishop, D., ve de Jong, P. F. (2017). Why are home literacy environment and children's reading skills associated? What parental skills reveal. Reading Research Quarterly, 52(2), 147-160.
  • van Gelderen, A., Schoonen, R., de Glopper, K., Hulstijn, J., Simis, A., Snellings, P. ve Stevenson, M. (2004). Linguistic knowledge, processing speed, and metacognitive knowledge in first-and second-Language reading comprehension: a componential analysis. Journal of Educational Psychology, 96(1), 19.
  • Vasant, P. (2013). Hybrid linear search, genetic algorithms, and simulated annealing for fuzzy non-linear industrial production planning problems. In Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance (pp. 87-109). IGI Global.
  • Veenendaal, N. J., Groen, M. A. ve Verhoeven, L. (2015). What oral text reading fluency can reveal about reading comprehension. Journal of Research in Reading, 38(3), 213-225.
  • Wickham, H. ve RStudio. (2020). Modelling Functions that Work with the Pipe. R package version 0.1.8.
  • Williams, J. P. (2003). Teaching text structure to improve reading comprehension. H. L. Swanson, K. R. Harris, & S. Graham (Eds.). In: Handbook of Learning Disabilities (pp. 293–305). New York: The Guilford Press.
  • Wise, S. L. (2017). Rapid-guessing behavior: Its identification, interpretation, and implications. Educational Measurement: Issues and Practice, 36(4), 52–61.
  • Wise, S. L. ve Kong, X. (2005). Response time effort: A new measure of examinee motivation in computer-based tests. Applied Measurement in Education, 18(2), 163–183.
  • Wise, S. L. ve DeMars, C. E. (2005). Low examinee effort in low stakes assessment: Problems and potential solutions. Educational Assessment, 10(1), 1-17.
  • Wolf, M. ve Katzir-Cohen, T. (2001). Reading fluency and its intervention. Scientific Studies of Reading, 5(3), 211-239.
  • Yang, C. Y., Jeng, J. T., Chuang, C. C. ve Tao, C. W. (2011, June). Constructing the linear regression models for the symbolic interval-values data using PSO algorithm. In Proceedings 2011 International Conference on System Science and Engineering (pp. 177-181). IEEE.
  • Yavuz, H. C. (2019). The Effects of Log Data on Students’ Performance. Journal of Measurement and Evaluation in Education and Psychology, 10(4), 378-390.
  • Yen, T. T. N. (2012). The effects of a speed reading course and speed transfer to other types of texts. RELC Journal, 43(1), 23-37.
  • Žegklitz, J. ve Pošík, P. (2015, July). Model selection and overfitting in genetic programming: Empirical study. In Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (pp. 1527-1528).

OKUMA BECERİLERİNE YÖNELİK MADDELERİ YANITLAMA HIZLARINI YORDAYAN ÖZELLİKLERİN BELİRLENMESİ

Yıl 2022, Sayı: 63, 311 - 332, 31.07.2022
https://doi.org/10.21764/maeuefd.1037681

Öz

Bu araştırmayla öğrencilerin okuma becerilerine yönelik maddeleri yanıtlama hızlarını yordayan özelliklerin belirlenmesi amaçlanmıştır. Araştırmanın çalışma grubunu, PISA 2015 programına katılan 5232 onbeş yaş grubu öğrenci oluşturmuştur. Araştırma verileri, PISA 2015 programı verileri üzerinden sağlanmış olup, genetik algoritmalar yöntemi kestirimine dayalı regresyon modeli esasıyla analiz edilmiştir. Analizler R programı üzerinden gerçekleştirilmiştir. Genetik algoritmalar yöntemi ile okuma becerilerine yönelik maddeleri yanıtlama hızlarını en iyi derecede yordayan değişkenlerden oluşan regresyon modeli için değişken seçim işlemi yapmak istenmiştir. Ulaşılan sonuçlara göre, cinsiyet, evdeki kitap sayısı, evde konuşulan dil, okuma becerisi, eylem sayısı ve okulda okuma becerileri için ayrılan haftalık ders saati değişkenlerinin öğrencilerin okuma becerilerine yönelik maddeleri yanıtlama hızlarını istatistiksel olarak anlamlı düzeyde yordadığı saptanmıştır. Yordama düzeyi anlamlı bulunan değişkenlerdeki farklılaşmanın öğrencilerin okuma becerilerini ölçen maddeleri yanıtlama hızlarında da anlamlı düzeyde farklılaşmaya yol açtığı anlaşılmıştır. Öğrencilerin okuma becerilerine yönelik maddeleri yanıtlama hızlarını istatistiksel olarak anlamlı yordayan değişkenlerin okuma becerilerini ölçen maddeleri yanıtlama hızlarındaki değişkenliğin %8.53’sini açıkladığı gözlenmiştir.

Kaynakça

  • Ahmed, A. B. ve Elaraby, I. S. (2014). Data mining: A prediction for student's performance using classification method. World Journal of Computer Application and Technology, 2(2), 43-47.
  • Altunkaynak, A. (2009). Sediment load prediction by genetic algorithms. Advances in Engineering Software, 40(9), 928-934.
  • Anderson, R. C., Wilson, P. T. ve Fielding, L. G. (1988). Growth in reading and how children spend their time outside of school. Reading Research Quarterly, 285-303.
  • Arnett, A. B., Pennington, B. F., Peterson, R. L., Willcutt, E. G., DeFries, J. C. ve Olson, R. K. (2017). Explaining the sex difference in dyslexia. Journal of Child Psychology and Psychiatry, 58(6), 719–727.
  • Arslan, B., Jiang, Y., Keehner, M., Gong, T., Katz, I. R. ve Yan, F. (2020). The effect of drag‐and‐drop item features on test‐taker performance and response strategies. Educational Measurement: Issues and Practice, 39(2), 96-106.
  • Başaran, M. (2013). Okuduğunu anlamanın bir göstergesi olarak akıcı okuma. Kuram ve Uygulamada Eğitim Bilimleri, 13(4), 2277-2290.
  • Büyüköztürk, Ş., Çakmak, E. K., Akgün, Ö. E., Karadeniz, Ş. ve Demirel, F. (2014). Bilimsel Araştırma Yöntemleri (16. bs.). Ankara: Pegem.
  • Cañizo, M. A., Suárez-Coalla, P. ve Cuetos, F. (2015). The role of reading fluency in children’s text comprehension. Frontiers in psychology, 6, 1810.
  • Dodonova Y. A. ve Dodonov Y. S. (2013). Faster on easy items, more accurate on difficult ones: Cognitive ability and performance on a task of varying difficulty. Intelligence, 41(1), 1-10.
  • Enders, C. K. (2010). Applied Missing Data Analysis. New York: The Guilford Press.
  • Fox, J. P. ve Marianti, S. (2017). Person-fit statistics for joint models for accuracy and speed. Journal of Educational Measurement, 54(2), 243-262.
  • Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Massachusetts: Addison-Wesley.
  • Goldhammer, F., Naumann, J. ve Greiff, S. (2015). More is not always better: The relation between item response and item response time in Raven’s matrices. Journal of Intelligence, 3(1), 21-40.
  • Greiff, S., Niepel, C., Scherer, R. ve Martin, R. (2016). Understanding students’ performance in a computer-based assessment of complex problem solving: An analysis of behavioral data from computer-generated log files. Computers in Human Behavior, 61(2016), 36-46.
  • Gürses, R. (1996). Okuma anlama üzerine. Atatürk Kültür, Dil ve Tarih Yüksek Kurumu Bülteni, 28(9), 98-103.
  • Haupt, R. L. ve Haupt, S. E. (1998). Practical Genetic Algorithms. USA: Willey-Interscience Publication.
  • Kaan, E., Ballantyne, J. C. ve Wijnen, F. (2015). Effects of reading speed on second-language sentence processing. Applied Psycholinguistics, 36(4), 799-830.
  • Kantemir, E. (1995). Yazılı ve Sözlü Anlatım. Ankara: Engin Yayınevi.
  • Karia, R. M., Ghuntla, T. P., Mehta, H. B., Gokhale, P. A. ve Shah, C. J. (2012). Effect of gender difference on visual reaction time: A study on medical students of Bhavnagar region. IOSR Journal of Pharmacy, 2(3), 452-454.
  • Kroehne, U., Hahnel, C. ve Goldhammer, F. (2019). Invariance of the response processes between gender and modes in an assessment of reading. Frontiers in Applied Mathematics and Statistics, 5, 2.
  • Kuhn, M., Wickham, H. ve RStudio. (2021). Preprocessing and Feature Engineering Steps for Modeling. R package version 0.1.17.
  • Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., R Core Team, Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C. ve Hunt, T. (2021). Classification and Regression Training. R package version 6.0-90.
  • Kushchu, I. (2002). Genetic programming and evolutionary generalization. IEEE Transactions on Evolutionary Computation, 6(5), 431-442.
  • Kyllonen, P. ve Zu, J. (2016). Use of response time for measuring cognitive ability. Journal of Intelligence, 4(4), 1-29.
  • Lai, F. K. (1993). The effect of a summer reading course on reading and writing skills. System, 21(1), 87-100.
  • Leardi, R., Boggia, R. ve Terrile, M. (1992). Genetic algorithms as a strategy for feature selection. Journal of Chemometrics, 6(5), 267-281.
  • Lee, Y. H. ve Haberman, S. J. (2016) Investigating test-taking behaviors using timing and process data. International Journal of Testing, 16(3), 240-267.
  • Lee, Y. H. ve Jia, Y. (2014). Using response time to investigate students’ test-taking behaviors in a NAEP computer-based study. Large-scale Assessments in Education, 2(8), 1-24.
  • Leisch, F. ve Dimitriadou, E. (2021). Machine Learning Benchmark Problems. R package version 2.1-3.
  • Marczyk, G., DeMatteo, D. ve Festinger, D. (2005). Essentials of Research Design and Methodology (Vol. 2). New York: John Wiley & Sons.
  • Michaelides, M. P., Ivanova, M. ve Nicolaou, C. (2020). The relationship between response-time effort and accuracy in PISA science multiple choice items. International Journal of Testing, 20(3), 187-205.
  • Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs (3.ed.). USA: Springer.
  • Minghua, S., Qingxian, X., Benda, Z. ve Feng, Y. (2017). Regression modelling based on improved genetic algoritm. Tehnicki vjesnik/Technical Gazette, 24(1).
  • National Research Council (2001). Knowing What Students Know: The Science and Design of educational Assessment. J. Pelligrino, N. Chudowsky, & R. Glaser (Eds.), Washington, DC: National Academy Press.
  • Oakhill, J., Cain, K. ve Elbro, C. (2015). Understanding and Teaching Reading Comprehension: A Handbook. New York: Routledge.
  • OECD. (2017). PISA 2015 Technical Report. Paris: OECD Publishing.
  • Örkcü, H. H. (2009). Ayırma analizine matematiksel programlama ve yapay sinir ağları yaklaşımları. Yayınlanmamış doktora tezi, Gazi Üniversitesi, Ankara.
  • Özdemir, M. (2017). Genetik algoritma ile doğrusal regresyonda tahmin amaçlı model seçimi. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 28, 213-233.
  • Pahmi, S., Saepudin, S., Maesarah, N., Solehudin, U. I. ve Wulandari (2018). Implementation of CART (classification and regression trees) algorithm for determining factors affecting employee performance. In: 2018 International Conference on Computing, Engineering, and Design (ICCED) 6-8 September 2018 (pp. 57-62). Bangkok: Institute of Electrical and Electronics Engineers (IEEE).
  • Paterlini, S. ve Minerva, T. (2010, June). Regression model selection using genetic algorithms. In Proceedings of the 11th WSEAS International Conference on Nural Networks and 11th WSEAS International Conference on Evolutionary Computing and 11th WSEAS International Conference on Fuzzy Systems (pp. 19-27). World Scientific and Engineering Academy and Society (WSEAS).
  • Ponce, H. R., Mayer, R. E., Sitthiworachart, J. ve López, M. J. (2020). Effects on response time and accuracy of technology-enhanced cloze tests: An eye-tracking study. Educational Technology Research and Development, 68(5), 2033-2053.
  • Rios, J. A., Guo, H., Mao, L. ve Liu, O. L. (2017). Evaluating the impact of careless responding on aggregated-scores: To filter unmotivated examinees or not?. International Journal of Testing, 17(1), 74-104.
  • Schnipke, D. L. ve Scrams, D. J. (2002). Exploring issues of examinee behavior: Insights gained from response-time analyses. Computer-Based Testing: Building the Foundation for Future Assessments, 237-266.
  • Scrucca, L. (2021). Genetic Algorithms. R package version 3.2.2.
  • Silge, J., Chow, F., Kuhn, M., Wickham, H. ve RStudio. (2021). General Resampling Infrastructure. R package version 0.1.1.
  • Şen, Z. ve Öztopal, A. (2001). Genetic algorithms for the classification and prediction of precipitation occurrence. Hydrological Sciences Journal, 46(2), 255-267.
  • Taguchi, N. (2005). Comprehending implied meaning in English as a foreign language. The Modern Language Journal, 89(4), 543-562.
  • Tanaka, H. ve Stapleton, P. (2007). Increasing reading input in Japanese high school EFL classrooms: An empirical study exploring the efficacy of extensive reading. The Reading Matrix, 7(1).
  • Temizkan, M. (2007). İlköğretim ikinci kademe Türkçe derslerinde okuma stratejilerinin okuduğunu anlama üzerindeki etkisi. Yayımlanmamış doktora tezi, Gazi Üniversitesi, Ankara.
  • Tolvi, J. (2004). Genetic algorithms for outlier detection and variable selection in linear regression models. Soft Computing, 8(8), 527-533.
  • Trejos, J., Villalobos-Arias, M. A. ve Espinoza, J. L. (2016). Variable selection in multiple linear regression using a genetic algorithm. In Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics (pp. 133-159). IGI Global.
  • van Bergen, E., van Zuijen, T., Bishop, D., ve de Jong, P. F. (2017). Why are home literacy environment and children's reading skills associated? What parental skills reveal. Reading Research Quarterly, 52(2), 147-160.
  • van Gelderen, A., Schoonen, R., de Glopper, K., Hulstijn, J., Simis, A., Snellings, P. ve Stevenson, M. (2004). Linguistic knowledge, processing speed, and metacognitive knowledge in first-and second-Language reading comprehension: a componential analysis. Journal of Educational Psychology, 96(1), 19.
  • Vasant, P. (2013). Hybrid linear search, genetic algorithms, and simulated annealing for fuzzy non-linear industrial production planning problems. In Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance (pp. 87-109). IGI Global.
  • Veenendaal, N. J., Groen, M. A. ve Verhoeven, L. (2015). What oral text reading fluency can reveal about reading comprehension. Journal of Research in Reading, 38(3), 213-225.
  • Wickham, H. ve RStudio. (2020). Modelling Functions that Work with the Pipe. R package version 0.1.8.
  • Williams, J. P. (2003). Teaching text structure to improve reading comprehension. H. L. Swanson, K. R. Harris, & S. Graham (Eds.). In: Handbook of Learning Disabilities (pp. 293–305). New York: The Guilford Press.
  • Wise, S. L. (2017). Rapid-guessing behavior: Its identification, interpretation, and implications. Educational Measurement: Issues and Practice, 36(4), 52–61.
  • Wise, S. L. ve Kong, X. (2005). Response time effort: A new measure of examinee motivation in computer-based tests. Applied Measurement in Education, 18(2), 163–183.
  • Wise, S. L. ve DeMars, C. E. (2005). Low examinee effort in low stakes assessment: Problems and potential solutions. Educational Assessment, 10(1), 1-17.
  • Wolf, M. ve Katzir-Cohen, T. (2001). Reading fluency and its intervention. Scientific Studies of Reading, 5(3), 211-239.
  • Yang, C. Y., Jeng, J. T., Chuang, C. C. ve Tao, C. W. (2011, June). Constructing the linear regression models for the symbolic interval-values data using PSO algorithm. In Proceedings 2011 International Conference on System Science and Engineering (pp. 177-181). IEEE.
  • Yavuz, H. C. (2019). The Effects of Log Data on Students’ Performance. Journal of Measurement and Evaluation in Education and Psychology, 10(4), 378-390.
  • Yen, T. T. N. (2012). The effects of a speed reading course and speed transfer to other types of texts. RELC Journal, 43(1), 23-37.
  • Žegklitz, J. ve Pošík, P. (2015, July). Model selection and overfitting in genetic programming: Empirical study. In Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (pp. 1527-1528).
Toplam 65 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

İzzettin Aydoğan 0000-0002-5908-1285

Selahattin Gelbal

Yayımlanma Tarihi 31 Temmuz 2022
Gönderilme Tarihi 16 Aralık 2021
Yayımlandığı Sayı Yıl 2022 Sayı: 63

Kaynak Göster

APA Aydoğan, İ., & Gelbal, S. (2022). OKUMA BECERİLERİNE YÖNELİK MADDELERİ YANITLAMA HIZLARINI YORDAYAN ÖZELLİKLERİN BELİRLENMESİ. Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi(63), 311-332. https://doi.org/10.21764/maeuefd.1037681