Applying The CHAID Algorithm to Analyze How Achievement is Influenced by University Students' Demographics, Study Habits, and Technology

Introduction Because developments in the field of information and communication technology (ICT) have led to a technology-based culture, educators are now educating a new generation of students. Accordingly, one of the important issues to be addressed when designing technology-oriented learning environments is learners' characteristics. With regard to this issue, the education profession faces two challenges. The first issue is that today's learners, who have grown up as digital natives and thus are being defined as the Net generation, are vastly different from the generations of learners that preceded current 21st-century learners (Oblinger & Oblinger, 2005; Prensky, 2001). The second issue is that children are experienced in a new form of play, that is, multiplayer computer and video games, and these experiences have shaped their preferences and capabilities regarding learning (Prensky, 2001). Therefore, to provide today's learners with a better education and a better learning context, educators should consider the characteristics of these new learners. Gender, working habits, motivation, media use, and book reading, as shown in Table 1, may influence Net generation students' academic performance. Motivation has been found to be a significant predictor of achievement among students (Ayub 2010; Chan, Wong, & Lo, 2012). In addition, gender has been correlated with achievement (Sarier, 2010; Veenstra & Kuyper, 2004). However, the use of technology by students has controversial results as well (Turner & Corucher, 2013; Junco, 2012). The research studies conducted on study habits did not always reveal consistent results in that while some found significant correlations between study habits and achievement (Yu, 2011), others did not (Olatoye & Ogunkola, 2008). The majority of the studies in Table 1, which have small sample sizes, focused on the dualities between motivation and achievement, and the use of technology and achievement. However, the current study deals with gender, university level and achievement, study habits and achievement, and the use of technology and achievement in one single study. Review of related literature The literature review of this study is presented beneath three subheadings: gender, university level, and achievement; study habits and achievement; and use of technology and achievement. Gender, university level, and achievement Conger and Long (2010) examined disadvantages that male students experienced with respect to grade-point average, credits earned, and persistence in college, and found that male students have lower GPAs and fewer credits in their first semester of college largely because they came to college with lower high-school grades. Female students' better high-school grades explain some of the gender disparity in performance, but differences in college course-taking and majors also explain gender gaps in credits, grades, persistence, and graduation. A study on persistence and success in an academic program at a community college revealed that students' GPAs, cumulative hours attempted, and cumulative hours completed were significant predictors of persistence and that young male students were a high-risk group (Stewart & Levin, 2001). Veenstra and Kuyper (2004) also revealed that gender was an important variable for academic achievement and female students were found to have higher levels of motivation. Finally, it was indicated that there was a significant difference between the genders in favor of female students regarding academic achievement in secondary education (Sarier, 2010). The literature did not indicate any finding about the effect of both university level and gender on achievement in one study. Study habits and achievement The study habits construct in the current study is twofold: working style (individual or studying in a group) and motivation (intrinsic or extrinsic). …