nach oben

Journal of Science Education and Technology

Erschienen in:

20.01.2022

The Mechanism of Influence Between ICT and Students’ Science Literacy: a Hierarchical and Structural Equation Modelling Study

verfasst von: Qing Guo, CuiLan Qiao, Bashirah Ibrahim

Erschienen in: Journal of Science Education and Technology | Ausgabe 2/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Information and communication technology (ICT) is key to educational development. This study explores the mechanism influencing the use of ICT on students’ science literacy. We utilized two-level hierarchical linear models and structural equation models to analyze data collected from the 2015 Program for International Student Assessment (PISA) in China. Results indicate that student-level and school-level ICT factors, in particular ICT interest, autonomy in using ICT, and ICT availability at school positively impact the development of students’ science literacy. Further, we found some notable interactions between school-level factors and student-level ICT variables. Moreover, there are structural relationships among ICT availability, ICT emotional perception, ICT learning usage behaviors, science self-efficacy, and science literacy. We also found that teacher-delivered science instruction has a negative moderating effect on ICT learning usage and science self-efficacy. These findings have important implications of how to integrate ICT in future science teaching practices.

Vorheriger Artikel Self-directed Science Learning During COVID-19 and Beyond

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Abdi, A. (2014). The effect of inquiry-based learning method on students’ academic achievement in science course. Universal Journal of Educational Research, 2(1), 37–41.CrossRef

Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior 1. Journal of Applied Social Psychology, 32(4), 665–683.CrossRef

Anderson, J. O., Lin, H. S., Treagust, D. F., Ross, S. P., & Yore, L. D. (2007). Using large-scale assessment datasets for research in science and mathematics education: Programme for International Student Assessment (PISA). International Journal of Science and Mathematics Education, 5(04), 591–614.CrossRef

Andrew, S. (1998). Self-efficacy as a predictor of academic performance in science. Journal of Advanced Nursing, 27, 596–603.CrossRef

Angrist, J., & Lavy, V. (2002). New evidence on classroom computers and pupil learning. The Economic Journal, 112(482), 735–765.CrossRef

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(01), 74–94.CrossRef

Bandura, A. (1986). Social foundations of thought and action: Asocial cognitive theory. Prentice Hall.

Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman.

Brečko, B. N., Kampylis, P., & Punie, Y. (2014). Mainstreaming ICT-enabled innovation in education and training in Europe. Retrieved from ftp://sjrcsvqpx102p.jrc.es/pub/EURdoc/EURdoc/JRC83502.pdf

Britner, S. L., & Pajares, F. (2001). Self-efficacy beliefs, motivation, race, and gender in middle school science. Journal of Women and Minorities in Science and Engineering, 7(04), 271–285.

Britner, S. L., & Pajares, F. (2006). Sources of science self-efficacy beliefs of middle school students. Journal of Research in Science Teaching, 43(5), 485–499.CrossRef

Bulut, O., & Cutumisu, M. (2018). When technology does not add up: ICT use negatively predicts mathematics and science achievement for Finnish and Turkish students in PISA 2012. Journal of Educational Multimedia and Hypermedia, 27(01), 25–42.

Chen, Y. H., & Zhao, K. Y. (2017). Xinxijishu dui Zhongxuesheng Ziwoxiaoneng de Cujinchengxiao [The effectiveness of information technology in promoting secondary school students’ science self-efficacy]. Open Education Research, 23(06), 60–70.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Eribaum.

Coleman, J. S. (1996). Equality of educational opportunity. US Department of Health, Education, and Welfare, Office of Education.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.CrossRef

Deci, E. L., & Ryan, R. M. (2008a). Facilitating optimal motivation and psychological well-being across life’s domains. Canadian Psychology/psychologie Canadienne, 49(1), 14.CrossRef

Deci, E. L., & Ryan, R. M. (2008b). Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology/psychologie Canadienne, 49(3), 182.CrossRef

Ellison, N. B., Steinfield, C., & Lampe, C. (2011). Connection strategies: Social capital implications of Facebook-enabled communication practices. New Media & Society, 13(6), 873–892.CrossRef

Erdogdu, F., & Erdogdu, E. (2015). The impact of access to ICT, student background and school/home environment on academic success of students in Turkey: An international comparative analysis. Computers & Education, 82, 26–49.CrossRef

Ertmer, P. A., & Ottenbreit-Leftwich, A. (2013). Removing obstacles to the pedagogical changes required by Jonassen’s vision of authentic technology-enabled learning. Computers & Education, 64, 175–182.CrossRef

Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the Technology Acceptance Model (TAM) to examine faculty use of Learning Management Systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(02), 210–232.

Fernández-Gutiérrez, M., Gimenez, G., & Calero, J. (2020). Is the use of ICT in education leading to higher student outcomes? Analysis from the Spanish autonomous communities. Computers & Education, 157, 103969.CrossRef

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research reading (pp. 129–381). Addison-Wesley.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research (JMR), 18(01), 39–50.CrossRef

Fuentes, M. D. C., & Gutiérrez, J. J. T. (2012). Does ICT improve Spanish students’ academic performance?. In Investigaciones de economía de la educación [número 7, julio 2012] (pp. 955–975). Asociación de Economía de la Educación.

Gagné, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26(4), 331–362.CrossRef

Gamazo, A., Martínez-Abad, F., Olmos-Migueláñez, S., & Rodríguez-Conde, M. J. (2018). Assessment of factors related to school effectiveness in PISA 2015. A Multilevel Analysis. Revista De Educación, 379, 56–84.

Gómez-Fernández, N., & Mediavilla, M. (2018). Do information and communication technologies (ICT) improve educational outcomes? Evidence for Spain in PISA 2015.

Grüsser, S. M., Thalemann, R., & Griffiths, M. D. (2006). Excessive computer game playing: Evidence for addiction and aggression? Cyberpsychology & Behavior, 10(02), 290–292.CrossRef

Habenicht, K. O., & Goldhammer, F. (2020). ICT engagement: A new construct and its assessment in PISA 2015. Large-Scale Assessments in Education, 8, 1–21.

Hair, J. F. (2010). Multivariate data analysis(7th ed.). London: Pearson Education

Hatlevik, O. E., Throndsen, I., Loi, M., & Gudmundsdottir, G. B. (2018). Students’ ICT self-efficacy and computer and information literacy: Determinants and relationships. Computers & Education, 118, 107–119.CrossRef

Hatos, A. (2020). Is using ICT at home good or bad for learning? A cross-country comparison of the impact of home use of ICT for entertainment and learning on PISA 2015 Science test results. https://doi.org/10.31219/osf.10/9dv5k

Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: Methodology in the Social Sciences. Kindle Edition, 193.

Hew, K. F., & Brush, T. (2007). Integrating technology into K-12 teaching and learning: Current knowledge gaps and recommendations for future research. Educational Technology Research and Development, 55(03), 223–252.CrossRef

Hox, J. (2010). Multilevel analysis: Techniques and applications. Routledge.CrossRef

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(01), 1–55.CrossRef

Hu, X., Gong, Y., Lai, C., & Leung, F. K. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1–13.CrossRef

James, L. R. (1982). Aggregation bias in estimates of perceptual agreement. Journal of Applied Psychology, 67(2), 219–228.CrossRef

Jordan, K. (2011). Framing ict, teachers and learners in Australian school education ict policy. The Australian Educational Researcher, 38(4), 417–431.CrossRef

Juhaňák, L., Zounek, J., Záleská, K., Bárta, O., & Vlčková, K. (2018). The Relationship between Students’ ICT Use and Their School Performance: Evidence from PISA 2015 in the Czech Republic. Orbis Scholae, 12(02), 37–64.CrossRef

Karahanna, E., & Limayem, M. (2000). E-mail and v-mail usage: Generalizing across technologies. Journal of Organizational Computing and Electronic Commerce, 10(01), 49–66.CrossRef

Kline, P. (1986). Personality inventories. In S. E. Newstead, S. H. Irvine, & P. L. Dann (Eds.), Human assessment: Cognition and motivation (pp. 109–112). Martinus Nijhoff.CrossRef

Kupermintz, H. (2002). Affective and conative factors as aptitude resources in high school science achievement. Educational Assessment, 8, 123–137.CrossRef

Lee, Y. H., Hsieh, Y. C., & Ma, C. Y. (2011). A model of organizational employees’ e-learning systems acceptance. Knowledge-Based Systems, 24(03), 355–366.CrossRef

Lei, J., & Zhao, Y. (2007). Technology uses and student achievement: A longitudinal study. Computers & Education, 49(2), 284–296.CrossRef

Livingstone, S. (2012). Critical reflections on the benefits of ICT in education. Oxford Review of Education, 38(1), 9–24.CrossRef

Luu, K., & Freeman, J. G. (2011). An analysis of the relationship between information and communication technology (ICT) and scientific literacy in Canada and Australia. Computers & Education, 56(04), 1072–1082.CrossRef

Ma, X., Ma, L., & Bradley, K. D. (2008). Using multilevel modeling to investigate school effects. In A. A. O’Connell & D. B. Maccoach (Eds.), Multilevel modeling of educational data (pp. 59–110). Information Age Publishing.

Martínez-Abad, F., Gamazo, A., & Rodriguez-Conde, M. J. (2018). Big Data in Education: Detection of ICT Factors Associated with School Effectiveness with Data Mining Techniques. the Sixth International Conference.

Meng, L., Qiu, C., & Boyd-Wilson, B. (2019). Measurement invariance of the ICT engagement construct and its association with students’ performance in China and Germany: Evidence from PISA 2015 data. British Journal of Educational Technology, 50(6), 3233–3251.CrossRef

Mora, T., Escardíbul, J. O., & Di Pietro, G. (2018). Computers and students’ achievement: An analysis of the One Laptop per Child program in Catalonia. International Journal of Educational Research, 92, 145–157.CrossRef

Odell, B., Galovan, A. M., & Cutumisu, M. (2020). The Relation Between ICT and Science in PISA 2015 for Bulgarian and Finnish Students. EURASIA Journal of Mathematics, Science and Technology Education, 16(6), em1846.

OECD. (2005). Are students ready for a technology-rich world? What PISA studies tell us. OECD Publishing.

OECD. (2009). PISA Data Analysis Manual. OECD Publishing.

OECD. (2017). PISA 2015 Assessment and Analytical Framework: Science, Reading, Mathematic, Financial Literacy and Collaborative Problem Solving. OECD Publishing.CrossRef

Papanastasiou, E. C., Zembylas, M., & Vrasidas, C. (2003). Can computer use hurt science achievement? The USA results from PISA. Journal of Science Education and Technology, 12(03), 325–332.CrossRef

Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(03), 150–162.

Petko, D., Cantieni, A., & Prasse, D. (2017). Perceived quality of educational technology matters: A secondary analysis of students’ ICT use, ICT-related attitudes, and PISA 2012 test scores. Journal of Educational Computing Research, 54(8), 1070–1091.CrossRef

Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585–1604.CrossRef

Rodrigues, M., & Biagi, F. (2017). Digital technologies and learning outcomes of students from low socio-economic background: An Analysis of PISA 2015. Publications Office of the European Union, Luxembourg. https://doi.org/10.2760/415251

Rouse, C. E., & Krueger, A. B. (2004). Putting computerized instruction to the test: A randomized evaluation of a “scientifically based” reading program. Economics of Education Review, 23, 323–338.CrossRef

Ryan, R. M., Chirkov, V. I., Little, T. D., Sheldon, K. M., Timoshina, E., & Deci, E. L. (1999). The American dream in Russia: Extrinsic aspirations and well-being in two cultures. Personality and Social Psychology Bulletin, 25(12), 1509–1524.CrossRef

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68.CrossRef

Schunk, D. H. (1985). Self-efficacy and classroom learning. Psychology in the Schools, 22, 208–223.CrossRef

Sheldon, K. M., Elliot, A. J., Ryan, R. M., Chirkov, V., Kim, Y., Wu, C., & Sun, Z. (2004). Self-concordance and subjective well-being in four cultures. Journal of Cross-Cultural Psychology, 35(2), 209–223.CrossRef

Skryabin, M., Zhang, J., Liu, L., & Zhang, D. (2015). How the ICT development level and usage influence student achievement in reading, mathematics, and science. Computers & Education, 85, 49–58.CrossRef

Specht, D. A. (1975). On the evaluation of causal models. Social Science Research, 4(02), 113–133.CrossRef

Spiezia, V. (2010). Does computer use increase educational achievements? Student-level evidence from PISA. OECD Journal: Economic Studies, 1–22.

Srijamdee, K., & Pholphirul, P. (2020). Does ICT familiarity always help promote educational outcomes? Empirical evidence from PISA-Thailand. Education and Information Technologies, 25(4), 2933–2970.CrossRef

Sugawara, H. M., & MacCallum, R. C. (1993). Effect of estimation method on incremental fit indexes for covariance structure models. Applied Psychological Measurement, 17(04), 365–377.CrossRef

Vincze, A. (2018). Exploring the Effect of Different Modes of ICT Use on School Performance Including Social Background. Belvedere Meridionale, 30(04), 181–190.CrossRef

Zhang, D., & Liu, L. (2016). How does ICT use influence students’ achievements in math and science over time? Evidence from PISA 2000 to 2012. Eurasia Journal of Mathematics, Science and Technology Education, 12(09), 2431–2449.CrossRef

Titel: The Mechanism of Influence Between ICT and Students’ Science Literacy: a Hierarchical and Structural Equation Modelling Study
verfasst von: Qing Guo
CuiLan Qiao
Bashirah Ibrahim
Publikationsdatum: 20.01.2022
Verlag: Springer Netherlands
Erschienen in: Journal of Science Education and Technology / Ausgabe 2/2022
Print ISSN: 1059-0145
Elektronische ISSN: 1573-1839
DOI: https://doi.org/10.1007/s10956-021-09954-9

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 2/2022

In Search of Assessment Shifts in Embodied Learning Science Research: a Review

Self-directed Science Learning During COVID-19 and Beyond

Using Reverse Engineering to Enhance Ninth-Grade Students’ Understanding of Thermal Expansion

Supporting Student System Modelling Practice Through Curriculum and Technology Design

“Because Subjects Don’t Exist in a Bubble”: Middle School Teachers Enacting an Interdisciplinary Curriculum

Does Learning from Giving Feedback Depend on the Product Being Reviewed: Concept Maps or Answers to Test Questions?

Premium Partner

Marktübersichten