Technology has become one of the most significant sources in reaching knowledge. However, it has also some threats such as internet addiction which affects individuals both physiologically and psychologically (Akin & Iskender, 2011). Internet addiction-based studies indicated its negative effects on the academic performance of the students (Leung & Lee, 2012; Samaha & Hawi, 2016). For example, Leung and Lee (2012) investigated the interrelationships among internet literacy, internet addiction symptoms, internet activities, and academic performance. The results indicated that there were two significant predictors for low academic performance which were the indicators of negative life consequences and preoccupation with the internet.

The current educational studies also suggest that the effective usage of STEM (the inclusion of Science, Technology, Engineering and Mathematics in education) can improve students’ attitudes towards mathematics and science, enable students the more permanent and deep learning, and provide opportunities for students to revise what they have learned (Ozcan & Koca, 2019). For example, some studies focused on STEM motivation, factors and their relationship with academic performance (Batdi, Talan & Semerci, 2019; Beede et al., 2011; English, 2016; Fortus, Krajcikb, Dershimerb, Marx, & Mamlok-Naamand, 2005; Wang 2013). Hence, STEM education might be a contributing factor for academic achievement. Another study conducted by Ozcan and Koca (2019) indicated that STEM integrated implementations affected students’ academic success and attitude toward STEM positively. The results of the other studies also suggested that most of the factors considering STEM education may have significant effect in mathematics and science achievement levels of learners. Thus, students’ attitudes towards STEM may be an important predictor to determine their mathematics and science achievement levels. 

There are many studies in the literature investigating the effects of different variables such as gender, school type, computer possession and study hours on mathematics achievement (Akyuz, 2013; Guzeller, Eser & Aksu, 2016; Lee et al, 2009), and on science achievement (Notten & Kraaykamp, 2009) or on both (Dunleavy & Heinecke, 2007).  However, there are also some contradictory results about the effects of some factors on math and science achievement in different studies such as computer possession. For example, while in some studies computer access affects mathematics (Lee et al., 2009) and science achievement (Dunleavy & Heinecke, 2007; Notten & Kraaykamp, 2009) positively and significantly, in other studies it does not affect math and science achievement significantly (Delen & Bulut, 2011). Hence, further studies suggested for investigating those relationships (Greenfield & Kraut, 2001).

It is seen that there are many studies about math and science achievement and some contributing factors such as gender, school type, STEM based instruction (Guzeller et, al. 2016; Lee et al, 2009). However, there are a limited number of studies about the relationship between STEM attitudes, and math and science achievement (Ozcan & Koca, 2019) and internet addiction and math and science achievement studies (Leung & Lee, 2012; Samaha & Hawi, 2016). Thus, in the current study, it is aimed to investigate how much the predictor variables which are school type, computer possession, weekly study hour, attitudes towards STEM, internet addiction predict students’ mathematics and science achievement. 

Participants The participants were 435 students from 10th, 11th and 12th grade students from five public high schools from two different cities of Turkey. In the study, there were two types of schools. The four high schools were Anatolian High Schools whereas one of the high schools was an Anatolian Religious High School. In Anatolian High schools there were more science and mathematics elective courses than Anatolian Religious High School. The number of male and female students were nearly equal. The students mostly came from lower socioeconomic status families and one third of the students did not have computers in their home. Research setting The research design of the study was the correlational research design to analyze the predictor variables for mathematics and science achievement. Before the data collection, each of the students were informed about the aim of the study and both of the instruments (Attitudes Towards STEM and Internet Addiction Scale). Only voluntary students participated in the study. Data collection Internet Addiction Scale (Gunuc, 2009) and Attitudes Towards STEM Scale (Ozcan & Koca, 2019) were used and both instruments included 35-items. Cronbach’s alpha was found as .96 for Internet Addiction Scale and as .91 for Attitudes Towards STEM Scale. Also, demographic information form was given to students to collect information about their grade level, father and mother education level, area, weekly study hour, computer possession, and how many hours they spent on computer to study, and grades from last year. Their grades on mathematics, biology, physics, and chemistry were obtained and the average of the last three was taken as their science achievement. Data analysis As Pituch and Stevens (2016) suggested, standard multiple regression was performed to predict dependent variables with set of predictor variables. In the study, the dependent variables were mathematics achievement and science achievement, whereas predictor variables consisted of the school type, grade level, computer possession, weekly study hour, total score of Attitudes Towards STEM Scale, total score of Internet Addiction Scale separately.

In mathematics achievement, the Pearson correlation coefficients were .256, -.194, and -.190 for the weekly study hour, the school type and total score of Internet Addiction Scale, respectively. Furthermore, in science achievement, the Pearson correlation coefficients were -.242, 239, .219, and -.215 for the type of the school, the weekly study hour, total score of Attitudes Towards STEM Scale and total score of Internet Addiction Scale, respectively. Since the coefficients were between 0.1 and 0.3, there is a small to moderate relationship between these predictors to both mathematics and science achievement. In the literature studies about internet addiction and academic success presented similar results because they found a negative relationship between internet addiction and academic achievement (Samaha & Hawi, 2016; Wentworth & Middleton, 2014). The results of multiple regression analysis showed weekly study hour and total score from Attitudes Towards STEM Scale were predictors for both mathematics and science achievement. For mathematics achievement, grade level was also a predictor variable. There was no significant difference between 11th and 12th graders but there was a significant difference between 10th and 12th graders. This might stem from the fact that Turkish students select their area at the beginning of 11th grade and therefore, in the 10th grade they take more basic mathematics courses. However, the school type was found as a predictor variable for science achievement. It might be differences in elective courses between schools that Anotolian high schools have more science based elective courses than Anatolian Religious High Schools. Similarly, Young and Fraser (1994) found that the level of school significantly affected science achievement. In conclusion, this study showed that students’ attitudes towards STEM is a good predictor for their achievement. Further studies might be conducted using other variables in order to predict mathematics and science achievement.

Selected References Akin, A., & Iskender, M. (2011). Internet addiction and depression, anxiety and stress. International online journal of educational sciences, 3(1), 138-148. Delen, E., & Bulut, O. (2011). The relationship between students’ exposure to technology and their achievement in science and math. The Turkish Online Journal of Educational Technology, 10(3), 311-317. Dunleavy, M., Dexter, S., & Heinecke, W. F. (2007). What added value does a 1: 1 student to laptop ratio bring to technology‐supported teaching and learning?. Journal of Computer Assisted Learning, 23(5), 440-452. Fortus, D., Krajcik, J., Dershimer, R. C., Marx, R. W., & Mamlok-Naaman, R. (2005). Design based science and real-world problem-solving. International Journal of Science Education, 27(7), 855-879. Gunuc, S. (2009). Developing internet addiction scale and examining the relationship between internet addiction and some demographic variables. Unpublished Master Thesis, Yüzüncü Yıl University, Social Science Institute, Van. Guzeller, C. O., Eser, M. T. & Aksu, G. (2016). The effect of high school type on university students’ mathematics achievement and tendency to think critically. Mersin University Journal of the Faculty of Education, 12(1), 223-236. Lee, S. M., Brescia, W., & Kissinger, D. (2009). Computer use and academic development in secondary schools. Computers in the Schools, 26(3), 224-235. Leung, L. & Lee, P. S. N. (2012). The influences of information literacy, internet addiction and parenting styles on internet risks. New Media and Society, 14(1), 117-136. Notten, N., & Kraaykamp, G. (2009). Home media and science performance: a cross-national study. Educational Research and Evaluation, 15(4), 367-384. Ozcan, H., & Koca, E. (2019). Adaptation of Attitudes Towards STEM Scale into Turkish: Validity and reliability study. Hacettepe University Journal of the Faculty of Education, 34(2), 387-401. Ozcan, H., & Koca, E. (2019). The effect of teaching “pressure” with STEM approach on seventh grade students’ academic performance and attitudes towards STEM. Education and Science, 44,(198). Pituch, K. A. & Stevens, J. P. (2016). Applied Multivariate Statistics for the Social Sciences (6th ed.). Routledge. Samaha, M., & Hawi, N. S. (2016). Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in Human Behavior, 57, 321-325. Wang, X. (2013). Why students choose STEM majors: motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 20(10), 1-41. Wentworth, D. K., & Middleton, J. H. (2014). Technology use and academic performance. Computers & Education, 78, 306-311.