Session Information
99 ERC ONLINE 24 A, Policy Studies and Politics of Education
Paper Session
MeetingID: 865 9314 6590 Code: BRKC45
Contribution
In Kazakhstan, there is a tendency for a gradual increase in the average students' scores from rural to urban. For instance, there is a difference of 25 points between rural and urban students in Kazakhstan, and the average for OECD countries is 32 points (OECD, 2013). National education quality assessment systems also confirm the results of international rankings. The average score according to the results of the Unified National Testing (UNT) of secondary school students from rural areas in 2016 was 78.2 points, which is lower than the national average (81.2 points). The results of EEEA-2018 (External evaluation of the educational achievements of students in secondary education in Kazakhstan) demonstrated that urban students' results exceed rural ones by 7.08 points (National Testing Center, 2018). Thus, one can observe an increase in the widening gap between rural and urban schools in Kazakhstan.
ICT can be one of the tools for eliminating the different forms of inequity in education. Especially currently, the global pandemic has shown and proven the importance of digital resources and literacy and their role in educating students regardless of location. The connection of ICT for students’ academic achievements has been supported by many studies (Basilaia & Kvavadze, 2020; Moreno-Correa, 2020; OECD, 2001). The impact of ICT on the difference in the performance of rural and urban students also seems significant. Although Fuchs & Woessman (2004) found a positive and significant correlation between computer availability in school and students’ performance in PISA, the correlation becomes small and insignificant when other characteristics of the school are taken into account. Therefore, given such characteristics as the school location, it is relevant to study reasons for low students’ performance in rural areas in comparison with urban. Perhaps one of the hypotheses for the rural students’ low performance is that Kazakhstan is not progressing fast when it comes to ICT implementation. Despite enormous investments in equipment and internet connection, teachers' competencies for using ICT in teaching, as well as students’ competencies for using ICT in learning are disputable, particularly in rural areas. Thus, it is important to pay attention to the analysis of internetization and the digitalization of schools (Nurbayev, 2019). Moreover, there has been done little to no research on the moderating effect of ICT on students’ performance and school location in Kazakhstan.
The purpose of this quantitative study is to investigate the relationship between academic performance and school location, the relationship between academic performance and ICT, and finally, whether ICT can help to bridge the differences between urban and rural schools? And what is the role of ICT in predicting Kazakhstani PISA 2018 digitally assessed reading, math, and science performance?
The analysis reveals that in Kazakhstan there is an association between rural school location and achieving lower test scores across all subjects. The models suggest that the ICT resources, ICT interest, and Perceived ICT competence have a positive effect on students’ achievement in math, science, and reading. After taking into account school location and ICT, students can perform better in rural schools. ICT adoption and increased student interest in it are more likely to improve the performance of rural students. When applied to a real-life situation, this conclusion suggests that a gradual increase in ICT resources and interest in ICT among students will contribute to an increase in the reading, math, and science performance through the introduction of ICT into the school and everyday life of students. Therefore, the role of ICT in neutralizing the difference between urban and rural schools in Kazakhstan has been identified, thereby it can reduce the inequity between urban and rural areas.
Method
The quantitative approach is used data from PISA 2018, which was coordinated by the Organization for Economic Cooperation and Development (OECD). Apart from knowledge assessment, PISA does provide insight into the access to ICTs for the students, both at school and at home, and the ways in which students use ICT. The use of the 2018 PISA database is an appropriate choice for a study examining large national samples through secondary analysis, especially for the purpose of assessment of the students’ performance with regards to ICT and school location (rural and urban). This study examines ICT and school-location variables for their impact on achievement while utilizing other ICT-related variables and school-contextual variables as controls. Analysis based on data from PISA 2018 (Kazakhstani dataset). The data of students' results in Mathematics (MATH), Reading Literacy (READ) and Science (SCIE) were studied. The data was taken from the student questionnaire (the ICT Familiarity Questionnaire) and the data from the school questionnaire (school locations) The data sample is a database of 15-16-year-old students from Kazakhstan who participated in PISA-2018. 19,507 randomly selected students from 616 schools in 16 regions of the country took part in this assessment (“PISA 2018 Database”, n.d.). The total number of schools in the dataset was 616, with 181 schools having fewer than 20 participating students. A total of 1,532 students studied at such schools. To answer research questions, the study used a proxy indicator of student outcomes (i.e., the dependent variable) based on student performance in math, reading, and science. For each type of literacy (math, reading, and science), a separate analysis was carried out. Data were analyzed using descriptive statistics, factorial analysis of variances (ANOVA), multiple and hierarchical regression. IBM SPSS program was employed to perform all the analyses. In addition, the multivariate analysis method allowed to examine the effect of the various predictors in one comprehensive model. Since hierarchical data (distribution of students by school) is the basis of the analysis, therefore, multilevel modeling was used. As mentioned above, a separate analysis was carried out for each assessment area (mathematics, reading literacy, and science).
Expected Outcomes
PISA results showed a consistent large-scale reading, math, and science underachievement among rural students in Kazakhstan. This study examined the impact of ICT on students’ outcomes in math, reading, and science literacy, and the role of ICT in neutralizing the difference between urban and rural schools in Kazakhstan. The results showed that students with higher ICT resources, ICT interest, and Perceived ICT competence, were more likely to be high-achieving students regardless of the school location. As a recommendation, the availability of ICT infrastructure, the Internet connection, teachers’ digital literacy, parents’ involvement in the education process, and facilitation of students learning using technology needs to be ensured in order to overcome the inequity that has emerged in the education system. Furthermore, the most important aspect for student achievement has become an interest in ICT which includes increasing student interest in technologies, the use of technology for studying purposes, and information literacy. The results reveal that increasing student interest in ICT leads to improvement of student outcomes. So, ICT has a significant effect on student performance and can be a transformative bridge that connects urban and rural school locations and reduce inequity in education The findings of this study may represent a significant source of information for the analysis of education for the Central Asian countries and other post-Soviet regions, as well as countries for which the difference between the students’ achievement of the urban and rural regions is significant.
References
Basilaia, G., & Kvavadze, D. (2020). Transition to online education in schools during a SARS-CoV-2 coronavirus (COVID-19) Pandemic in Georgia. Pedagogical Research, 5(4), 123-135. Fuchs T. & Woessmann L. (2004). Computers and student learning: Bivariate and multivariate evidence on the availability and use of computers at home and at school. Brussels Econ,47, 359–386. Moreno-Correa, S.-M. (2020). Educational innovation in the times of the Coronavirus. Salutem Scientia Spiritus Magazine, 6(1), 14-26. National Testing Center (2018). Info graphic presentation of external evaluation of educational achievements // https://testcenter.kz/ru/stats/voud-so/2018/ Nurbayev, Z. (2019). Kazakhstan: Unequal struggle for equality in school. Cabar. https://cabar.asia/en/kazakhstan-unequal-struggle-for-equality-in-school#_ftn1 OECD. (2001). Education policy analysis 2001. Centre for Educational Research and Innovation (CERI). Paris, France. OECD. (2013). PISA excellence through equity: Giving every student the chance to succeed: (Volume II). OECD. (2013). What makes urban schools different? (PISA in Focus No. 28). Paris, France. PISA 2018 Database. (n.d.). https://www.oecd.org/pisa/data/2018database/
Search the ECER Programme
- Search for keywords and phrases in "Text Search"
- Restrict in which part of the abstracts to search in "Where to search"
- Search for authors and in the respective field.
- For planning your conference attendance you may want to use the conference app, which will be issued some weeks before the conference
- If you are a session chair, best look up your chairing duties in the conference system (Conftool) or the app.