تیلور، کاترین اس. (1398). روایی و رواسازی. ترجمه جلیل یونسی. تهران: انتشارات دانشگاه علامه طباطبائی.
سرمد، زهره، بازرگان، عباس و حجازی، الهه. (1391). روشهای تحقیق در علوم رفتاری. تهران: نشر آگاه.
References
Baepler, P., & Murdoch, C. J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching and Learning, 4(2), 1-9.
Barnes, T. M. (2003). The q-matrix method of fault-tolerant teaching in knowledge assessment and data mining.
Berkhin, P. (2006). A survey of clustering data mining techniques. In Grouping multidimensional data (pp. 25-71). Springer, Berlin, Heidelberg.
Brunet, J. P., Tamayo, P., Golub, T. R., & Mesirov, J. P. (2004). Metagenes and molecular pattern discovery using matrix factorization. Proceedings of the national academy of sciences, 101(12), 4164-4169.
Carthy, A., Gray, G., McGuinness, C., & Owende, P. (2014). A review of psychometric data analysis and applications in modelling of academic achievement in tertiary education.
Casalino, G., Castiello, C., Del Buono, N., Esposito, F., & Mencar, C. (2017, July). Q-matrix extraction from real response data using nonnegative matrix factorizations. In International Conference on Computational Science and Its Applications (pp. 203-216). Springer, Cham.
Chang, W. C., & Yang, H. C. (2009). Applying IRT to Estimate Learning Ability and K-means Clustering in Web based Learning. JSW, 4(2), 167-174.
Chavent, M., Kuentz, V., Liquet, B., & Saracco, L. (2011). ClustOfVar: An R package for the clustering of variables. arXiv preprint arXiv:1112.0295.
Freitas, A. A. (2002). Data mining and knowledge discovery with evolutionary algorithms. Springer Science & Business Media.
Hand, D. J., Mannila, H., & Smyth, P. (2001). Retrieval by Content.
Hutchins, M. J., & Sutherland, J. W. (2008). An exploration of measures of social sustainability and their application to supply chain decisions. Journal of cleaner production, 16(15), 1688-1698.
Jose, P. E. (2013). Doing statistical mediation and moderation. Guilford Press.
Kerr, D., & Chung, G. K. (2012). Identifying key features of student performance in educational video games and simulations through cluster analysis. JEDM| Journal of Educational Data Mining, 4(1), 144-182.
Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data: an introduction to data mining (Vol. 4). John Wiley & Sons.
Man, K., Harring, J. R., & Sinharay, S. (2019). Use of data mining methods to detect test fraud. Journal of Educational Measurement, 56(2), 251-279.
Mislevy, R. J., Behrens, J. T., Dicerbo, K. E., & Levy, R. (2012). Design and discovery in educational assessment: Evidence-centered design, psychometrics, and educational data mining. Journal of educational data mining, 4(1), 11-48.
Nguyen, D. Q., Nguyen, T. D., Nguyen, D. Q., & Phung, D. (2017). A novel embedding model for knowledge base completion based on convolutional neural network. arXiv preprint arXiv:1712.02121.
Peña-Ayala, A. (Ed.). (2013). Educational data mining: applications and trends (Vol. 524). Springer.
Pardos, Z. A., & Dadu, A. (2018). dAFM: Fusing Psychometric and Connectionist Modeling for Q-matrix Refinement. JEDM Journal of Educational Data Mining, 10(2), 1-27.
Reimann, C., Filzmoser, P., Garrett, R., & Dutter, R. (2011). Statistical data analysis explained: applied environmental statistics with R. John Wiley & Sons.
Schumann, J. A. (2005). Data mining methodologies in educational organizations. University of Connecticut.
Wu, H. (2013). Comparison of General Diagnostic Models (GDM) and Bayesian Networks Using a Middle School Mathematics Test.
Yu, Z. (2009). Optimization techniques in data mining with applications to biomedical and psychophysiological data sets. Theses and Dissertations, 274.