Agasisti, Tommaso and Gil-Izquierdo, María and Han, Seong Won (2017): ICT use at home for school-related tasks: what is the effect on a student’s achievement? Empirical evidence from OECD PISA data.
Preview |
PDF
MPRA_paper_81343.pdf Download (2MB) | Preview |
Abstract
In this paper, we have employed data from the OECD’s Programme for International Student Assessment (PISA, 2012 edition) on the EU-15 countries in order to investigate the relationship between (i) the way in which students use ICT at home for school-related purposes and (ii) their test scores in reading, mathematics and science. By employing two different econometric techniques – namely, propensity score matching and instrumental variables – we can provide evidence that in most countries there is an association between using computers intensely for homework and achieving lower test scores across all subjects. No clear pattern emerges for differences between students with higher socio-economic status (SES) and their low-SES counterparts, although some models suggest that the negative effect of using ICT at home is slightly greater for high-SES students. These findings suggest that a more cautious approach should be taken with regards to the wide-spread use of digital innovation as a means to support students’ out-of-school work. Such an indication can potentially suggest that teachers should be trained to integrate this practice effectively into their strategies for assigning homework.
Item Type: | MPRA Paper |
---|---|
Original Title: | ICT use at home for school-related tasks: what is the effect on a student’s achievement? Empirical evidence from OECD PISA data |
Language: | English |
Keywords: | Digital learning, educational production function (EPF), OECD-PISA, propensity score matching, instrumental variables |
Subjects: | I - Health, Education, and Welfare > I2 - Education and Research Institutions > I21 - Analysis of Education |
Item ID: | 81343 |
Depositing User: | Dr María Gil-Izquierdo |
Date Deposited: | 18 Sep 2017 17:36 |
Last Modified: | 27 Sep 2019 19:57 |
References: | Agasisti, T. and Murtinu, S. (2012). ‘Perceived’ competition and performance in Italian secondary schools: new evidence from OECD–PISA 2006. British Educational Research Journal, 38(5), 841-858. Attewell, P. and Battle, J. (1999). Home computers and school performance. The Information Society, 15(1), 1-10. Bedard, K. and Dhuey, E. (2006). The persistence of early childhood maturity: International evidence of long-run age effects, The Quarterly Journal of Economics, 1437-1472. Biagi, F. and Loi, M. (2013). Measuring ICT use and learning outcomes: Evidence from recent econometric studies. European Journal of Education, 48(1), 28-42. Bowers, A. J. and Berland, M. (2013). Does recreational computer use affect high school achievement?. Educational Technology Research and Development, 61(1), 51-69. Brown, G., Micklewright, J., Schnepf, S. V. and Waldmann, R. (2007). International surveys of educational achievement: how robust are the findings?. Journal of the Royal Statistical Society: Series A (statistics in society), 170(3), 623-646. Caliendo, M. and Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22(1), 31-72. Cheung, A. C. and Slavin, R. E. (2013). The effectiveness of educational technology applications for enhancing mathematics achievement in K-12 classrooms: A meta-analysis. Educational Research Review, 9, 88-113. Choi, Á., Calero, J. and Escardíbul, J. O. (2012). Private tutoring and academic achievement in Korea: An approach through PISA-2006, KEDI Journal of Educational Policy, 9(2), 299-302. Cunha, F. and Heckman, J. (2007). The technology of skill formation (No. w12840). NBER Working Paper. National Bureau of Economic Research. De Witte, K. and Rogge, N. (2014). Does ICT matter for effectiveness and efficiency in mathematics education? Computers & Education, 75, 173-184. Dronkers, J. and Avram, S. (2010). A cross-national analysis of the relations of school choice and effectiveness differences between private-dependent and public schools. Educational Research and Evaluation, 16(2), 151-175. Edwards, S., Marin and A. G. (2015). Constitutional rights and education: An international comparative study, Journal of Comparative Economics, 43(4), 938-955. Fairlie, R. W. (2005). The effects of home computers on school enrollment. Economics of Education Review, 24(5), 533-547. Fairlie, R. W., Beltran, D. O. and Das, K. K. (2010). Home computers and educational outcomes: evidence from the NLSY97 and CPS. Economic Inquiry, 48(3), 771-792. Fairlie, R. W. and Robinson, J. (2013). Experimental evidence on the effects of home computers on academic achievement among schoolchildren. American Economic Journal: Applied Economics, 5(3), 211-240. Fariña, P., San Martín, E., Preiss, D. D., Claro, M. and Jara, I. (2015). Measuring the relation between computer use and reading literacy in the presence of endogeneity. Computers & Education, 80, 176-186. Fuchs, T. and Woessmann, L. (2007) What accounts for international differences in student performance? A re-examination using PISA data, Empirical Economics, 32, 433–464. Gui, M., Micheli, M. and Fiore, B. (2014). Is the internet creating a 'learning gap' among students? Evidence from the Italian PISA data. Italian Journal of Sociology of Education, 6(1), 1-24. Gürsakal, S., Murat, D. and Gürsakal, N. (2016). Assessment of PISA 2012 results with quantile regression analysis within the context of inequality in educational opportunity. Alphanumeric: The Journal of Operations Research, Statistics, Econometrics and Management Information Systems, 4(2), 41-54. Guo, S. and Fraser, M. W. (2010): Propensity Score Analysis. Statistical Methods and Applications. SAGE publications. London. Haelermans, C. and Ghysels, J. (2013). The Effect of an Individualized Online Practice Tool on Math Performance-Evidence from a Randomized Field Experiment. TIER Working Paper. Hanushek, E. A. and Woessmann, L. (2006). Does educational tracking affect performance and inequality? Differences-in-differences evidence across countries. The Economic Journal, 116, 63–76. Hermans, R., Tondeur, J., van Braak, J. and Valcke, M. (2008). The impact of primary school teachers’ educational beliefs on the classroom use of computers. Computers & Education, 51(4), 1499-1509. Jackson, L. A., von Eye, A., Biocca, F. A., Barbatsis, G., Zhao, Y. and Fitzgerald, H. E. (2006). Does home internet use influence the academic performance of low-income children? Developmental Psychology, 42(2), 429-435. Jager, A. and Lokman, A. H. (2000). The Impact of ICT in Education: The Role of the Teacher and Teacher Training (pp. 22-25). Stoas Research. Jensen, P. and Rasmussen, A. W. (2011). The effect of immigrant concentration in schools on native and immigrant children's reading and math skills, Economics of Education Review, 30(6), 1503-1515. Jiang, F., McComas and W. F. (2015). The effects of inquiry teaching on student science achievement and attitudes: Evidence from propensity score analysis of PISA data. International Journal of Science Education, 37(3), 554-576. Jürges, H., Schneider, K. and Büchel, F. (2005). The effect of central exit examinations on student achievement: Quasi‐experimental evidence from TIMSS Germany. Journal of the European Economic Association, 3(5), 1134-1155. Lee, B. (2014). The influence of school tracking systems on educational expectations: a comparative study of Austria and Italy, Comparative Education, 50(2), 206-228. Li, X., Atkins, M. S. and Stanton, B. (2006). Effects of home and school computer use on school readiness and cognitive development among Head Start children: A randomized controlled pilot trial. Merrill-Palmer Quarterly, 52(2), 239-263. Malamud, O. and Pop-Eleches. (2011). Home computer use and the development of human capital. The Quarterly Journal of Economics, 126(2), 987-1027. OECD (2009) PISA Data Analysis Manual. SPSS® Second Edition. Organisation for Economic Co-operation and Development (2014). PISA 2012 technical report. Paris: OECD Publishing. Organisation for Economic Co-operation and Development (2015). Students, computers and learning: Making the connection. Paris: OECD Publishing. Santín, D. and Sicilia, G. (2016). Does family structure affect children's academic outcomes? Evidence for Spain. The Social Science Journal, 53(4), 555-572. Smeets, E. (2005). Does ICT contribute to powerful learning environments in primary education?. Computers & Education, 44(3), 343-355. Spiezia, V. (2011). Does computer use increase educational achievements? Student-level evidence from PISA. OECD Journal: Economic Studies, 2010(1), 1-22. Stock, J., Wright, J. and Yogo, M. (2002). A survey of weak instruments and weak identification in Generalized Method of Moments, Journal of Business and Economic Statistics, 20(4), pp. 518–29. Torrubia, M. J. M., de Embún, D. P. X. and Sancho, J. M. G. (2016) Educación pública y educación concertada en España: aportaciones desde un enfoque cuasi experimental aplicado a las escuelas de educación primaria de Aragón. Encuentro Economía Pública. Ourense, 2016. Vandenberghe, V. and Robin, S. (2004). Evaluating the effectiveness of private education across countries: a comparison of methods. Labour Economics, 11(4), 487-506. Vigdor, J. L. and Ladd, H. F. (2014). Scaling the digital divide: Home computer technology and student achievement. Economic Inquiry, 52(3), 1103-1197. West, M. R. and Woessmann, L. (2010). ‘Every Catholic Child in a Catholic School’: Historical Resistance to State Schooling, Contemporary Private Competition and Student Achievement across Countries. The Economic Journal, 120(546), 229-255. Wittwer, J. and Senkbeil, M. (2008). Is students’ computer use at home related to their mathematical performance at school?. Computers & Education, 50(4), 1558-1571. Woessmann, L. and West, M. (2006). Class-size effects in school systems around the world: Evidence from between-grade variation in TIMSS. European Economic Review, 50(3), 695-736. Wooldridge, J. M. (2009). Econometrics. Cengage Learning India Private Limited. Zhang, M. (2015). Internet use that reproduces educational inequalities: Evidence from big data. Computers & Education, 86, 212-223. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/81343 |