24 SES 14 JS, ICT and Mathematics Education Part 2
Joint Paper Session NW 16 and NW 24 continued from 24 SES 11
This contribution addresses the use of information and communication technologies (ICT) for teaching and learning in primary mathematics education and sheds light on the extent to which the use of ICT in the mathematics domain is related to contextual prerequisites at school level. This includes, for example, the proportion of students with a low economic status or the proportion of students with an immigrant background. Both factors are shown to be relevant in the context of mathematics education (cf. Boonen et al., 2014). In order to answer the research question, analysis is conducted using the most recent database of the Trends in International Mathematics and Science Study is utilized (TIMSS 2015, cf. Mullis, Martin, Foy, & Hooper, 2016).
In view of ongoing developments related to an information society and in the light of the pace of technological developments, the ability to use computers is regarded as a substantial prerequisite for mastering individual future challenges (Fraillon et al., 2014). Hence, researchers, educators and educational stakeholders argue that teaching computer and information literacy (CIL) is a central goal of scholastic education even in the primary education sector. In this context, research often focuses on the question of whether or not the use of ICT in teaching and learning has beneficial effects for students learning and which factors at student and teacher level have an influence on the use of ICT in teaching and learning and on students’ CIL. Furthermore, the school context and factors on the school level play a crucial role in students’ acquisition of digital and subject-related competences (Eickelmann, Gerick, & Koop, 2017; Gerick, Eickelmann, & Bos, 2017). Overall, these and other research findings focus predominantly on secondary schools.
Most theoretical models that conceptualize teaching and learning with regard to ICT make individual and contextual variables a main focus. Examples include the contextual model of the International Computer and Information Literacy Study (ICILS 2013; cf. ibid) or the model used by Biagi and Loi (2013) to examine factors that affect students’ ICT use and school performance. However, most research in the field does in fact include individual characteristics of students and their families (Eickelmann, 2011) and for example school characteristics in terms of ICT resources (Gerick et al., 2017), teachers attitudes towards using ICT (Eickelmann & Vennemann, 2017), and/or computerization policies (Biagi & Loi, 2013) and does not account for compositional variables of the school that have been shown to have a potential influence on teaching and learning in primary education (Barr & Dreeben, 1983; Schofield, 2010).
Therefore, this contribution addresses the following research gaps: a) the relevance of school level factors in primary schools in the use of ICT in mathematics learning and b) the effects of school compositional variables on the use of ICT. The following research questions are focused in detail:
- To what extent can the use of ICT for mathematics learning in primary school be attributed to individual and/or school characteristics?
- Do variables relating to the social student body composition relate to ICT use in mathematics learning and how do they relate to effects of individual background characteristics?
Whereas the first research question focuses on the question of the relevance of school characteristics in the use of ICT in mathematics learning, research question two focuses on the effect of variables relating to social school body composition.
The research presented in this paper is carried out as secondary analysis of data from the representative multi-stratified cross-sectional samples for selected countries (Czech Republic, Germany, Denmark) from the TIMSS 2015 study (Martin, Mullis, Foy, & Hooper, 2016). Using hierarchical linear modelling techniques (HLM, cf. Raudenbush & Bryk, 2002) the relations between the use of ICT in mathematics learning in 4th grade, student background variables such as gender, migratory background, students’ socioeconomic status (SES) and cultural capital, the availability of ICT for teaching and learning and characteristics of the social composition of schools are explored. The latter comprises the proportion of boys, the proportion of students with a high level of cultural capital, the proportion of students with a low SES, and the proportion of students with an immigrant background. Analysis is conducted using the representative student samples from three European countries that have shown to have different (curricular) prerequisites for the use of ICT in primary mathematics education (Czech Republic: N = 5.202, Denmark: N = 3.710 and Germany N = 3.948) and a 3-step analytical approach as described in the following paragraphs. In a first step, a so-called Nullmodel (One-Way ANOVA with Random Effects) without any predictors was specified in order to determine the proportion of variation of ICT use that can be attributed to and explained by characteristics at the individual and at the school level. In step 2, those social-related compositional variables of schools that were generated by aggregating students’ background characteristics at school level were included in the model without covariates at the individual level. In the third step, those characteristics of schools (availability of ICT and variables relating to social school body composition) and those of the students (such as gender, socioeconomic status or their cultural capital) are modelled together in order to determine whether compositional variables are able to explain variation in ICT use beyond individual characteristics. All analyses in this contribution were calculated with the statistical modelling software MPlus 7.0 (Muthén & Muthén, 2012) taking into account appropriate student and school weightings that are suitable for conducting cross-country analyses (Mohammadpour, Shekarchizadeh, & Kalantarrashidi, 2015; Rutkowski, Gonzalez, Joncas, & von Davier, 2010).
Overall, results indicate that school compositional variables are of varying relevance in all countries. First analyses regarding the relevance of school compositional characteristics (research question 1) for the use of ICT in primary mathematics education show that in Germany less variation in ICT use can be explained by school characteristics (ρMaths=.091) compared to the other educational systems (Czech Republic: ρMaths=.232 vs. Denmark: ρMaths=.372). When compositional variables of schools and individual characteristics of students are modelled together (research question 2) in order to determine whether or not the school body has an additional effect beyond individual characteristics, the result emerges that individual characteristics remain of high relevance in the Czech Republic. When school compositional variables are statistically controlled for, it's only in the Czech Republic that effects of individual characteristics (gender and immigrant status) can be observed. In Denmark and Germany, however, there are no relations of individual characteristics and students’ use of ICT for mathematics education. In line with the majority of models conceptualizing the factors of ICT use and school performance, the availability of ICT is an important predictor of ICT use. In fact, the availability of ICT in mathematics education turned out to be important in every country included in the research. Regarding compositional effects, the results vary from country to country. In Germany, for example, none of the school composition variables relates to student use of ICT in primary mathematics education. In the Czech Republic and Denmark single effects (proportion of students with migratory background or the proportion of boys) can be observed. In general, the results stress the relevance of school body composition in some European countries (Czech Republic and Denmark) for the use of ICT in primary mathematics education and need to be discussed in the light of ongoing developments towards inclusive education in Europe.
Barr, R., & Dreeben, R. (1983). How schools work. Chicago, IL: University of Chicago Press. Biagi, F., & Loi, M. (2013). Measuring ICT use and Learning outcomes. Evidence from recent econometric studies. European Journal of Education, 48(1), 28-42. Boonen, T., Speybroeck, S., de Bilde, J., Lamotte, C., Van Damme, J., & Onghena, P. (2014). Does it matter who your schoolmates are? An investigation of the association between school composition, school processes and mathematics achievement in the early years of primary education. British Educational Research Journal, 40(3), 441-466. Eickelmann, B. (2011). Supportive and hindering factors to a sustainable implementation of ICT in schools. Journal for Educational Research Online, 3, 75-103. Eickelmann, B., Gerick, J., & Koop, C. (2017). ICT use in mathematics lessons and the mathematics achievement of secondary school students by international comparison: Which role do school level factors play? . Journal for Education and Information Technologies, 22(4), 1527-1551. Eickelmann, B., & Vennemann, M. (2017). Teachers‘ attitudes and beliefs regarding ICT in teaching and learning in European countries. European Educational Research Journal. doi: 10.1177/1474904117725899 Fraillon, J., Ainley, J., Schuz, W., Friedman, T., & Gebhardt, E. (2014). Preparing for life in a digital age. The IEA International Computer and Information Literacy Study international report. Cham: Springer. Gerick, J., Eickelmann, B., & Bos, W. (2017). School-level Predictors for ICT Use in Schools and Students’ Computer and Information Literacy. Large-scale Assessments in Education. Martin, M.O., Mullis, I.V.S., Foy, P., & Hooper, M. (2016). TIMSS 2015 International Results in Science. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College. Mohammadpour, E., Shekarchizadeh, A., & Kalantarrashidi, S.A. (2015). Multilevel Modeling of Science Achievement in the TIMSS Participating Countries. The Journal of Educational Research, 108(6), 449-464. Mullis, I.V.S., Martin, M.O., Foy, P., & Hooper, M. (2016). TIMSS 2015 International Results in Mathematics. Chestnur Hill, MA: TIMSS & PIRLS International Study Center, Lynch School of Educatio, Boston College. Muthén, L.K., & Muthén, B.O. (2012). Mplus 7. Los Angeles, CA: Muthén & Muthén. Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical Linear Models. Application and Data Analysis Methods (2. ed.). Thousand Oaks, CA: Sage. Rutkowski, L., Gonzalez, E., Joncas, M., & von Davier, M. (2010). International large-scale assessment data: Issues in secondary analysis and reporting. Educational Researcher, 39(2), 142-151. Schofield, J.W. (2010). International evidence on ability grouping with curriculum differentiation and the achievement gap in secondary schools. Teachers College Record, 112(5), 1492-1528.
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