حسنآبادی، حمیدرضا، طلایی، ابراهیم، سیدمیرزایی جهقی، آزاده، و برارپور، گلرخ. (1396). شواهد تجربی از تعامل متن و خواننده در دانشآموزان جهشی پایة چهارم: آیا سواد خواندن معیاری برای تسریع تحصیلی محسوب میشود؟.
روانشناسی تحولی: روانشناسان ایرانی،
14(54)، 135-146.
https://journals.iau.ir/article_539236.html
زارع، حسین، و نقش، سیمین. (1395). بررسی سهم متغیرهای سطح دانش آموز و معلم بر عملکرد خواندن دانشآموزان پایه چهارم براساس داده های پرلز 2006.
دوماهنامه علمی - پژوهشی رهیافتی نو در مدیریت آموزشی، 7(26)، 52-39.
https://dorl.net/dor/20.1001.1.20086369.1395.7.26.3.4
ساداترضایی، مهناز، کیامنش، علیرضا، و زهرا، نقش. (1393). بررسی سهم متغیرهای سطح دانش آموز و مدرسه بر عملکرد خواندن دانشآموزان براساس داده های پرلز 2006.
فصلنامه تعلیم و تربیت،
30(4)، 140-123.
http://qjoe.ir/article-1-189-fa.html
قائدامینی، رقیه، کیامنش، علیرضا، و قربانی، رقیه. (1393). رابطه بین وضعیت اقتصادی-اجتماعی خانواده, فعالیت های خواندن در خانه, خودپنداره و نگرش دانش آموزان به خواندن با عملکرد سواد خواندن دانش آموزان (بر اساس مطالعات پرلز 2006).
پژوهش در برنامه ریزی درسی (دانش و پژوهش در علوم تربیتی-برنامهریزی درسی)،
11(41)، 103-88.
https://sanad.iau.ir/Journal/jsre/Article/898018
نامداری پژمان، مهدی، و کیامنش، علیرضا. (1390). ارتباط عوامل شناختی فردی و خانوادگی با ابعاد درک مطلب خواندن دانش آموزان چهارم ابتدایی شرکت کننده در مطالعه پرلز 2006.
مطالعات برنامه درسی،
5(20)، 57-37.
https://sid.ir/paper/101172/fa
Allington, R. L., & McGill-Franzen, A. M. (2021). Reading volume and reading achievement: A review of recent research.
Reading Research Quarterly, 56(S1), 231–238.
https://doi.org/10.1002/rrq.404
Bai, Y., Liu, J., Wang, S., & Yang, F. (2018). Machine learning applied to star–galaxy–QSO classification and stellar effective temperature regression.
The Astronomical Journal, 157(1), Article 9.
https://doi.org/10.3847/1538-3881/aaf009
Bender, R. (2009). Introduction to the use of regression models in epidemiology. In M. Verma (Ed.),
Cancer epidemiology: Methods in molecular biology (Vol. 471, pp. 179-195). Humana Press.
https://doi.org/10.1007/978-1-59745-416-2_9
Botchkarev, A. (2019). Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology.
Interdisciplinary Journal of Information, Knowledge, and Management, 14, 45–79.
https://doi.org/10.48550/arXiv.1809.03006
Chen, G., Kumar, V., Huang, R., & Kong, S. C. (Eds.). (2015).
Emerging issues in smart learning. Springer Berlin Heidelberg.
https://doi.org/10.1007/978-3-662-44188-6
Cho, B. Y., Hwang, H., & Jang, B. G. (2021). Predicting fourth grade digital reading comprehension: A secondary data analysis of (e) PIRLS 2016.
International Journal of Educational Research, 105, Article 101696.
https://doi.org/10.1016/j.ijer.2020.101696
Cunningham, P., Cord, M., & Delany, S. J. (2008). Supervised learning. In M. Cord & P. Cunningham (Eds.),
Machine learning techniques for multimedia: Case studies on organization and retrieval (pp. 21-49). Springer Berlin Heidelberg.
https://doi.org/10.1007/978-3-540-75171-7_2
Ebbs, D., Flicop, S., Hidalgo, M. M., & Netten, A. (2023). Systems and instrument verification in PIRLS 2021. In M. von Davier, I. V. S. Mullis, B. Fishbein, & P. Foy (Eds.),
Methods and procedures: PIRLS 2021 technical report (pp. 5.1-5.24). Boston College, TIMSS & PIRLS International Study Center.
https://doi.org/10.6017/lse.tpisc.tr2103.kb2485
El Naqa, I., Li, R., & Murphy, M. J. (Eds.). (2015).
Machine learning in radiation oncology: Theory and applications. Springer International Publishing.
https://doi.org/10.1007/978-3-319-18305-3
Fishbein, B., Yin, L., & Foy, P. (2024).
PIRLS 2021 user guide for the international database (2nd ed.). Boston College, TIMSS & PIRLS International Study Center.
https://pirls2021.org/data
Fox, J. (2015). Applied regression analysis and generalized linear models. Sage Publications.
George, D., & Mallery, M. (2021).
SPSS for Windows step by step: A simple guide and reference (17th ed.). Routledge.
https://doi.org/10.4324/9781003205333
Geske, A., & Ozola, A. (2008). Factors influencing reading literacy at the primary school level.
Problems of Education in the 21st Century, 6, 71-77.
https://www.scientiasocialis.lt/pec/node/112
Issa, M. A., & Nadal, K. L. (2011). Homoscedasticity. In S. Goldstein & J. A. Naglieri (Eds.),
Encyclopedia of child behavior and development (p. 752). Springer.
https://doi.org/10.1007/978-0-387-79061-9_1382
Iyengar, S., & Ball, D. (2007).
To read or not to read: A question of national consequence. Washington, DC: National Endowment for the Arts.
https://www.arts.gov/impact/research/publications/read-or-not-read-question-national-consequence
Jiang, T., Gradus, J. L., & Rosellini, A. J. (2020). Supervised machine learning: A brief primer.
Behavior Therapy, 51(5), 675-687.
https://doi.org/10.1016/j.beth.2020.05.002
Lenkeit, J., Chan, J., Hopfenbeck, T. N., & Baird, J. A. (2015). A review of the representation of PIRLS related research in scientific journals.
Educational Research Review, 16, 102-115.
https://doi.org/10.1016/j.edurev.2015.10.002
Liu, D., Wang, L., Xu, Z., Li, M., Joshi, R. M., Li, N., & Zhang, X. (2023). Understanding Chinese children’s word reading by considering the factors from cognitive, psychological and ecological factors.
Contemporary Educational Psychology, 73, Article 102163.
https://doi.org/10.1016/j.cedpsych.2023.102163
Lopes, J., Oliveira, C., & Costa, P. (2022). Determinantes escolares y de los estudiantes en el rendimiento lector: Un análisis multinivel con estudiantes portugueses.
Revista de Psicodidáctica, 27(1), 29-37.
https://doi.org/10.1016/j.psicod.2021.05.001
Luan, J. (2002). Data mining and knowledge management in higher education: Potential applications (ED474143). ERIC.
https://eric.ed.gov/?id=ED474143
Ma, L., Xiao, L., & Hau, K. T. (2022). Teacher feedback, disciplinary climate, student self-concept, and reading achievement: A multilevel moderated mediation model.
Learning and Instruction, 79, Article 101602.
https://doi.org/10.1016/j.learninstruc.2022.101602
Mahesh, B. (2020). Machine learning algorithms: A review.
International Journal of Science and Research (IJSR), 9(1), 381-386.
https://doi.org/10.21275/ART20203995
Marôco, J. (2021). What makes a good reader? Worldwide insights from PIRLS 2016.
Reading and Writing, 34(1), 231-272.
https://doi.org/10.1007/s11145-020-10068-8
Mullis, I. V. S., & Martin, M. O. (Eds.). (2019).
PIRLS 2021 assessment frameworks. Boston College, TIMSS & PIRLS International Study Center.
https://timssandpirls.bc.edu/pirls2021/frameworks/
Reynolds, K., & Martin, M. O. (2023). Updating the PIRLS 2021 instruments for describing the contexts for student learning. In M. von Davier, I. V. S. Mullis, B. Fishbein, & P. Foy (Eds.),
Methods and procedures: PIRLS 2021 technical report (pp. 2.1-2.8). Boston College, TIMSS & PIRLS International Study Center.
https://doi.org/10.6017/lse.tpisc.tr2102.kb8382
Schneider, A., Hommel, G., & Blettner, M. (2010). Linear regression analysis: Part 14 of a series on evaluation of scientific publications.
Deutsches Ärzteblatt International, 107(44), 776-782.
https://doi.org/10.3238/arztebl.2010.0776
Schober, P., & Vetter, T. R. (2020). Confounding in observational research.
Anesthesia & Analgesia, 130(3), 636-643.
https://doi.org/10.1213/ANE.0000000000004627
Yin, L., & Reynolds, K. A. (2023). Creating and interpreting the PIRLS 2021 context questionnaire scales. In M. von Davier, I. V. S. Mullis, B. Fishbein, & P. Foy (Eds.),
Methods and procedures: PIRLS 2021 technical report (pp. 15.1-15.161). Boston College, TIMSS & PIRLS International Study Center.
https://doi.org/10.6017/lse.tpisc.tr2103.kb6994
