Ecer 2021 pre-conference resource

1. Investigating the relationship of
Science and Mathematics
Achievement with Attitudes Towards
STEM and Internet Addiction
Sevil Sezer, Nil Arabaci, Busra Aksoz, Fatma Arikan,
Sevda Yardelen-Damar
Presenting Author: Fatma ARIKAN
fatma.arikan@boun.edu.tr

2. The Outline
• Introduction
• Methodology
• Results
• Discussion & Implications
• Selected References

3. INTRODUCTION

4. In this study mathematics and science
achievement were investigated in terms of
contributing variables; internet addiction,
attitude toward STEM, time spent on
computer to study, school type, and grade
level.

5. § Technology has 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).
§ The educational studies suggest that the effective usage of STEM can improve students’
attitudes towards mathematics and science (Ozcan & Koca, 2019). In addition, STEM-based
education could influence students’ mathematics and science achievement positively (Ozcan
& Koca, 2019; Batdi, Talan & Semerci, 2019).
§ Morever, other contributing factors were investigated such as gender, school type, computer
possession and study hours on mathematics achievement (Akyuz, 2013; Guzeller, Eser &
Aksu, 2016; Lee, Brescia & Kissinger, 2009), and on science achievement (Notten &
Kraaykamp, 2009) or on both (Dunleavy, Dexter & Heinecke, 2007). However, there were
some contradictory results about the effects of some factors on mathematics and science
achievement in different studies such as computer possession (Dunleavy, Dexter & Heinecke,
2007; Lee et al, 2009; Notten & Kraaykamp, 2009).

6. Purpose &
Significance of
the Study
• It is seen that there are number of studies about math and
science achievement and some contributing factors such as
gender, school type, computer posession, STEM based
instruction (Akyuz, 2013; Delen & Bulut, 2011; Guzeller et, al.
2016; Lee et al, 2009; Notten & Kraaykamp, 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).
• Therefore, this study aimed to investigate how much the
predictor variables (school type, grade level, weekly study hour
on computer, attitudes towards STEM, internet addiction)
predict students’ mathematics and science achievement

7. METHODOLOGY

8. Research
Questions
How much the predictor variables which are the
school type, grade level, weekly study hour,
attitudes towards STEM, and internet addiction
predict students’ mathematics and science
achievement?

9. The Research Design
• The research design of the study is multiple linear regression (MLR) which is a
statistical technique that uses several explanatory variables to predict the
outcome of a response variable (Pituch & Stevens, 2016)
• In the study, the dependent variables were mathematics achievement and
science achievement separately; whereas predictor variables consisted of the
school type, grade level, weekly study hour (on computer), attitudes towards
STEM, and internet addiction.

10. 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.
Anatolian High Schools
Anatolian Religious High School
Table 1 Demographic information about the participants
Categories Sub-categories n %
Gender Female
Male
236
197
54.5
45.5
School Types Anatolian High School
Anatolian Imam Hatip
High School
201
234
46.2
53.8
Grade Level 10
11
12
165
184
86
37.9
42.3
19.8

11. Instruments
Attitudes Towards STEM Scale
● The original of the scale was developed
by The Friday Institute for Educational
Innovation (2012)
● The Turkish version of this scale was
conducted by Ozcan and Koca (2019)
● 5-point Likert scale
● Total 37 items under four sub-categories
○ “Mathematics”
○ “Science”
○ “Engineering and technology”,
○ “21st-century skills”
The Internet Addiction Scale
● The scale was developed by Gunuc
(2009)
● 5-point Likert scale
● Total 35 items under four sub-
categories
○ “Withdrawal”
○ “Controlling difficulty”
○ “Disorder in functionality”
○ “Social isolation”

12. Data Collection
• During a week (December 2019), the data was collected from five different
schools from 10th, 11th and 12th grade students.
• Demographic information form was given to students to collect information
about their grade level, area, weekly study hour on computer, and grades from
previous term.
• The Attitudes Towards STEM Scale and the Internet Addiction Scale were
applied in one class hour to only voluntary participants.

13. Data Analysis
Standard multiple linear regression (MLR) was performed to
predict dependent variables with the set of predictor variables
• Data screening procedures
• Checking MLR assumptions
• Reporting and interpreting

14. RESULTS

15. The relationship between dependent variables and
predictor variables

16. The Regression results for predicting mathematics
achievement
It was concluded that attitudes toward STEM, weekly study hour and grade level could significantly
predict mathematics achievement (p <.050).

17. The Regression results for predicting science achievement
It was concluded that attitudes toward STEM, weekly study hour, and school type could significantly
predict science achievement (p =.000 <.050).

18. DISCUSSION & IMPLICATIONS

19. Discussion & Implications
• The study concluded correspondent results with
previous studies that there was a significant
positive relationship between attitudes toward
STEM and science and mathematics achievement
(Ozcan & Koca, 2019) and there was a significant
negative relationship between internet addiction
and academic achievement (Samaha & Hawi, 2016;
Wentworth & Middleton, 2014).
• In addition, the study showed that there is a
positive significant relationship between study
hour on computer and science and mathematics
education while there is a significant negative
relationship between internet addiction and
science and mathematics achievement. Thus,
further studies are needed to reveal how to use
computer and internet to improve science and
mathematics achievement as Subrahmanyam,
Greenfield, Kraut, and Gross (2001) suggested
before.

20. Discussion & Implications
• The results of multiple linear regression analysis
showed that the variables weekly study hour (on
computer) and attitudes towards STEM were
significant positive 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 in the 10th grade they
take more basic mathematics courses.
• The school type was found as a significant predictor
variable for only science achievement. It might
stem from differences in elective courses between
schools that Anatolian high schools have more
science based elective courses than Anatolian
Religious High Schools. Similarly, Young and Fraser
(1994) found that the type of school significantly
affected science achievement.
• Finally, students’ attitude towards STEM was a
significant predictor for their mathematics and
science achievement while internet addiction is not
a significant predictor. In addition, all significant
predictors in the models were low predictors.
Hence, further studies suggested using other
variables to generate more explanatory models to
predict mathematics and science achievement.

21. SELECTED REFERENCES
Akin, A., & Iskender, M. (2011). Internet addiction and depression, anxiety and stress. International online journal of educational sciences, 3(1),
138-148.
Batdi, V., Talan, T., & Semerci, C. (2019). Meta-analytic and meta-thematic analysis of STEM education. International Journal of Education in
Mathematics, Science and Technology (IJEMST), 7(4), 382-399.
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.
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. (2012). The influences of information literacy, internet addiction and parenting styles on internet risks. New media & society,
14(1), 117-136.
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), 201-227.
Samaha, M., & Hawi, N. S. (2016). Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers
in Human Behavior, 57, 321-325.
Subrahmanyam, K., Greenfield, P., & Kraut, R. E., & Gross, E. (2001). The impact of computer use on children's and adolescents' development,
Journal of Applied Developmental Psychology, 22(1), 7-30.