ARCHIVES

[1] R. L. Ahadi, H. Haapala, and A. Vihavainen, “Exploring machine learning methods to automatically identify students in need of
assistance,” in Proc. 11th Annu. Int. Conf. Int. Comput. Educ. Res., 2015, pp. 121–130.
[2] K. Quille and S. Bergin, “Programming: Further factors that influence success,” in Psychology of Programming Interest Group
(PPIG). Cambridge, U.K.: Univ. Cambridge, 2016.
[3] C. Y. Ko and F. Y. Leu, “Analyzing attributes of successful learners by using machine learning in an undergraduate computer
course,” in Proc. 32nd IEEE Int. Conf. Adv. Inf. Netw. Appl. (AINA-2018), Krakow, Poland, 2018, pp. 801–806.
[4] S. Kotsiantis and D. Kanellopoulos, “Association rules mining: A recent overview,” Int. Trans. Comput. Sci. Eng., vol. 32, no. 1, pp.
71–82, 2006.
[5] J.-L. Hung and K. Zhang, “Revealing online learning behaviors and activity patterns and making predictions with data mining
techniques in online teaching,” J. Online Learn. Teach., vol. 4, no. 4, pp. 426–436, 2008.
[6] A. Ezen-Can, K. E. Boyer, S. Kellogg, and S. Booth, “Unsupervised modeling for understanding MOOC discussion forums: A
learning analytics approach,” in Proc. 5th Int. Conf. Learn. Anal. Knowl., 2015, pp. 146–150.
[7] J.-L. Hung, M. C. Wang, S. Wang, M. Abdelrasoul, Y. Li, “Identifying at-risk students for early interventions—A time-series
clustering approach,” IEEE Trans. Emerg. Topics Comput., vol. 5, no. 1, pp. 45–55, Jan.–Mar. 2017.
[8] C. Romero, M.-I. López, J.-M. Luna, S. Ventura, “Predicting students’ final performance from participation in on-line discussion
forums,” Comput. Educ. vol. 68, pp. 458–472, Oct. 2013.
[9] R. Asif, A. Merceron, S. A. Ali, and N. G. Haider, “Analyzing undergraduate students’ performance using educational data
mining,” Comput. Educ., vol. 113, pp. 177–194, Oct. 2017.
[10] S. Amershi and C. Conati, “Unsupervised and supervised machine learning in user modeling for intelligent learning
environments,” in Proc. 12th Int. Conf. Intell. User Interfaces, 2007, pp. 72–81.
[11] B. J. Zimmerman, “Attaining of self-regulation: A social cognitive perspective,” in Handbook of Self-Regulation, Research, and
Applications, M. Boekaerts, P. Pintrich, and M. Zeidner, Eds. Orlando, FL, USA: Academic, 2000, pp. 13–39.
[12] B. J. Zimmerman, “Investigating self-regulation and motivation: Historical background, methodological developments, and future
prospects,” Amer. Educ. Res. J., vol. 45, no. 1, pp. 166–183, 2008.
[13] A. Bandura, Social Learning Theory. Oxford, U.K.: Prentice-Hall, 1977.
[14] A. Bandura, Self-Efficacy: The Exercise of Control. New York, NY, USA: Freeman, 1997.
[15] R. Lynch and M. Dembo, “The relationship between self-regulation and online learning in a blended learning context,” Int. Rev. Res.
Open Distance Learn., vol. 5, no. 2, pp. 1–16, 2004.
[16] M. V. J. Veenman, B. H. A. M. Van Hout-Wolters, and P. Afflerbach, “Metacognition and learning: Conceptual and
methodological considerations,” Metacogn. Learn., vol. 1, pp. 3–14, Mar. 2006.
[17] P. R. Pintrich, “The role of goal orientation in self-regulated learning,” in Handbook of Self-Regulation, M. Boekaerts, P. R.
Pintrich, and M. Zeidner, Eds. San Diego, CA, USA: Academic, 2000, pp. 451–502.
[18] D. H. Schunk, “Self-regulated learning: The educational legacy of Paul R. Pintrich,” Educ. Psychol., vol. 40, no. 2, pp. 85–94,
2005.
[19] A. Kitsantas, A. Winsler, and F. Huie, “Self-regulation and ability predictors of academic success during college: A predictive
validity study,” J. Adv. Acad., vol. 20, no. 1, pp. 42–68, 2008.
[20] D. Compeau and C. Higgins, “Computer self-efficacy: Development of a measure and initial test,” MIS Quart., vol. 19, no. 2, pp.
189–211, 1995.