Using Methods of Association Rules Mining Optimizationin in Web-Based Mobile-Learning System

Data mining techniques applied in educational environments: Literature Review

“…In 2008, Ouyang Y. and Zhu M. in "eLORM: Learning Object Repository based Relationship Mining" (Ouyang & Zhu, 2008) show how to discover patterns to recommend learning objects to students using sequential patterns; Ranjan J. and Khalil S. in "Conceptual Framework of Data Mining Process in Management Education in India: An Institutional Perspective" (Ranjan & Khalil, 2008) use decision trees and Bayesian networks to support the admission process and to analyze the quality of the education process and student performance in India; Otherwise, Merceron A. and Yacef K. employ association rules to analyze learning data and determine whether students use academic resources and which of them may have greater impact, work published in "Interestingness Measures for Association Rules in Educational Data " (Merceron & Yacef, 2008); using this same technique, Ventura S., Romero C. and Hervas C. in " Analyzing rule evaluation measures with educational datasets: a framework to help the teacher" (Ventura, Romero, & Hervás, 2008) analyze measures assessment rules of educational data in order to identify interesting patterns; Chanchary F.H, Haque I. and Khalid M. S. in "Web Usage Mining to Evaluate the Transfer of Learning in a Web-Based Learning Environment" (Chanchary, 2008a) find relations in access to LMS (Learning Management System) and student behavior to identify patterns Internet usage by the students; Vialardi C., Bravo J. and Ortigosa A. on "Improving AEH courses through log analysis" ("Improving AEH Courses through Log Analysis .," 2015) explain how to improve the design of the course from recommendations generated by log analysis of courses. Using this same technique, Zheng, S. Xiong S., Huang Y. and Wu S. in "Using methods of association rules mining optimization in mobile web-based learning system" (Zheng, S., Xiong, Huang, & Wu, 2008) explain how to find relationships between attributes and solution strategies adopted by students in a mobile learning system based on the web. In the same year, Pechenizkiy M., Calders T., Vasilyeva E. and De Bra P. in "the Student Assessment Data Mining: Lessons Drawn from a Small Scale Case Study" (Pechenizkiy, Calders, Vasilyeva, & De Bra, 2008) show a proposal to the use in the extraction data of student assessment, this, using clustering, decision trees and association rules.…”

“…Association rules (Sanjeev & Zytkow, 1995), (Ha et al, 2000), (Yu et al, 2001), (Tsai et al, 2001), (Li & Yamanishi, n.d.), (F. Wang, 2002), (Zaiane, 2002), , (Minaei-Bidgoli et al, 2004), ), (J. Lu, 2004), (Ramli, 2005), (Markellou et al, 2005), (Retalis et al, 2006), (Vranić et al, 2007), (Baruque et al, 2007), (Merceron & Yacef, 2008), (Chanchary, 2008b), ("Improving AEH Courses through Log Analysis .," 2015), (Zheng, S. et al, 2008), (Cristóbal Romero, González, et al, 2009) (Psaromiligkos et al, 2011), (V. , (Baradwaj & Pal, 2012b), (Parack et al, 2012), (Varun Kumar & Chadha, 2012), (Cristóbal Romero, Zafra, et al, 2013), (Jha & Ragha, 2013), (Chalaris et al, 2014), (Belsis et al, 2014) Linear regression (Hämäläinen & Vinni, n.d.), (Thai-nghe et al, 2010) Table 3. Classification of papers by data mining techniques Source: Own work Figure 5 presents the graph of the number of EDM studies classified by techniques used.…”