Antecedents of Students’ Self-Regulatory Strength in Technology-Rich School Environments
Abstract
The internet activity of adolescents has increased to a considerable extent over the past few years. A key question is how students are able to regulate their study efforts in technology-rich classrooms. With the introduction of internet access in the classroom, a conflict of motivations may ensue between short-term rewards of playing games, interacting on social media or surfing the net and the long-term rewards of academic achievement. The purpose of this article is to explore the antecedents of students’ self-regulatory strength. The antecedents are students’ school motivation and school-related factors (use of internet as a learning resource at school, as well as distinct quality aspects of the teaching: teacher expectations, explanatory skills and classroom management). Regression analysis and structural equation modelling (SEM) were carried out based on 3400 student (15-17 year olds) answers to a questionnaire administered in 60 secondary schools. First, the regression analysis shows significant associations between the regressors and students’ regulatory strength. Second, the SEM analysis shows that any positive effect of the teaching on students’ self-regulation depends to a significant extent on the attitudes of the students towards the school as an institution. Third, our results show that the provision of the internet as a teaching resource induces a motivational conflict between recreational internet activity and school-related academic work. This conflict has a clear negative effect on students’ regulatory strength in academic work. The conclusion must therefore be that it is difficult to make use of the many internet affordances for school learning within schools without a critical awareness of the potential negative side effects on students’ self-regulatory strength.Â
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Abramovich, S. (2013). Computers in Mathematics Education: An Introduction, Computers in the Schools: Interdisciplinary Journal of Practice, Theory, and Applied Research, 30 (1-2), 4-11.
Ainslie, G. (2001). Breakdown of will. Cambridge,MA:Cambridge University Press.
Angrist, J. & Lavy, V. (2002). New Evidence on Classroom Computers and Pupil Learning, The Economic Journal,112 (482),735–765.
Aaronson D., Barrow L., Sander W. (2007). Teachers and student achievement in the Chicago Public High School. Journal of Labor Economics, 25(1), 95–135.
Blikstad-Balas, M. (2012). Digital Literacy in Upper Secondary School - What Do Students Use Their Laptops for During Teacher Instruction? Nordic Journal of Digital Literacy, 2(7), 81-96.
Brante, G. (2009). Multitasking and synchronous work: Complexities in teacher work. Teaching and Teacher Education, 25 (3), 430–436
Clark,R.C. & Mayer, R.E. (2011). E-Learning and the Science of Instruction. New York: Wiley.
Debele, M. & Plevyak, L. (2012). Conditions for Successful Use of Technology in Social Studies Classrooms, Computers in the Schools: Interdisciplinary Journal of Practice, Theory, and Applied Research, 29 (3), 285-299.
Doyle, W. (2006). Ecological approaches to classroom management. In Edmund Emmer,Edward Sabornie,Carolyn M. Evertson,Carol S. Weinstein (eds.): Handbook of classroom management: Research, practice, and contemporary issues, 97-125. Routledge, New York.
Duckworth, A.L. & Seligman, M.E. (2005). Self-Discipline Outdoes IQ in Predicting Academic Performance of Adolescents. Psychological Science, 16(12): 939-944.
Duckworth, A. L. & Seligman, M. E. P. (2006). Self-Discipline Gives Girls the Edge: Gender in Self-Discipline, Grades, and Achievement Test Scores, Journal of Educational Psychology, 98(1), 198-208.
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual review of psychology, 53(1), 109-132.
Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., & Mac Iver, D. (1993). Development during adolescence: the impact of stage-environment fit on young adolescents' experiences in schools and in families. American psychologist, 48(2), 90.
Eccles, J.S. (2014). Families, Schools, and Developing Achievement-Related Motivations and Engagement. (pp. 665-691) In Grusec, J. E., & Hastings, P. D. (Eds.). (2014). Handbook of socialization: Theory and research. Guilford Publications.
Emmer, E. T., & Stough, L. M. (2001). Classroom management: A critical part of educational psychology, with implications for teacher education. Educational Psychologist, 36(2), 103-112.
European Commission (2013) Survey of Schools: ICT in Education. https://ec.europa.eu/digital-agenda/sites/digital-agenda/files/KK-31-13-401-EN-N.pdf
Finnish National Board of Education (2014). Learning objectives and core contents of education. Finnish National Board of Education, Helsinki.
Fried, C.B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50 (3), 906–914
Goldhaber D., Hansen M. (2010). Race, gender, and teacher testing: How informative a tool is teacher licensure testing? American Educational Research Journal, 47(1), 218–251.
Greeno, J. G. (2006). Learning in activity. In The Cambridge handbook of the learning sciences,Sawyer, R. K. (ed.),79–96. New York: Cambridge.
Ito, M., Baumer, S., Bittanti, M., Boyd, D., Cody, R., Herr-Stephenson, B., Horst, H. A., Lange, P. G., Mahendran, D., Martinez, K. Z., Pascoe, C. J., Perkel, D., Robinson, L., Sims, C. & Tripp, L. (2010). Hanging out, messing around, and geeking out - Kids Living and Learning with New Media. Cambridge, MA: The MIT Press
Kaplan, A. and Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1): 59–68.
Kline, R. B. (2006). Structural equation modeling. New York: Guilford.
Nunnally, J. C., Bernstein, I. H., & Berge, J. M. T. (1967). Psychometric theory. New York: McGraw-Hill.
Luckin, R., Clark, W., Graber, R., Logan, K., Mee, A. and Oliver, M. (2009). Do Web 2.0 tools really open the door to learning? Practices, perceptions and profiles of 11–16-year-old students. Learning, Media and Technology, 34(2): 87–104. (doi:10.1080/17439880902921949)
Ludvigsen, S.R. (2012). What counts as knowledge: learning to use categories in computer environments, Learning, Media and Technology, 37(1):40-52
Mayer, R. E. & Moreno, R. (2002). Aids to computer-based multimedia learning. Learning and Instruction, 12 (1), 107–119.
Mischel, W. & Ayduk, O. (2011). Willpower in a Cognitive-Affective Processing System: The dynamics of Delay of Gratification In Vohs, K. D., & Baumeister, R. F. (Eds.). (2011). Handbook of self-regulation: Research, theory, and applications. Guilford Press. Pp. 83-105.
Nunnally, J.C., & Bernstein, I.H. (1994) Psychometric theory. NewYork: McGraw-Hill.
Ophir, E., Nass, C., & Wagner, A. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences of the United States of America, 106(37), 15583-15587.
Rivkin S., Hanushek E., Kain J. (2005). Teachers, schools, and academic achievement. Econometrica, 73(2), 417–458.
Rockoff, J. E. (2004). The impact of individual teachers on student achievement: evidence from panel data. The American Economic Review, 94(2), 247–252.
Rosenthal, R. & Jacobson, L. (1968). Pygmalion in the classroom, The Urban Review, 3(1), 16-20.
Rutten, N., van Joolingen, W. R., & van der Veen, J. T. (2012). The learning effects of computer simulations in science education. Computers & Education, 58, 136-153.
Salomon, G., & Almog, T. (1998). Educational psychology and technology: A matter of reciprocal relations. The Teachers College Record, 100(2), 222-241.
Salomon, G. (1983). The differential investment of mental effort in learning from different sources. Educational Psychologist, 18(1), 42-50.
Salomon, G., & Perkins, D. (2005). Do technologies make us smarter? Intellectual amplification with, of and through technology, in D. Preiss, & R. Sternberg (Eds), Intelligence and technology. Routledge, Cambridge.
Sana, F., Weston, T. & Cepeda, N. J (2013) Laptop multitasking hinders classroom learning for both users and nearby peers, Computers & Education, 62, 24-31.
Selwyn, N. (2011). Education and technology: Key issues and debates. London: A&C Black.
Skolverket (2013). Curriculum for the upper secondary school, Skolverket: Stockholm.
Smetana, L. K., & Bell, R. L. (2012). Computer simulations to support science instruction and learning: A critical review of the literature. International Journal of Science Education, 34(9), 1337-1370.
Smeets, E. & Mooij,T. (2001). Pupil-centred learning, ICT, and teacher behaviour: observations in educational practice, British Journal of Educational Technology, 32 (4), 403–417.
Søby, M. (2013). Synergies for Better Learning – Where Are We Now? Nordic Journal of Digital Literacy, 8(01-02), 3- 11.
Säljö, R. (2005). Læring i praksis: et sosiokulturellt perspektiv. Oslo: Cappelen Forlag.
Tangney, J. P., Baumeister, R. F. & Boone, A. L. (2004). High self-control predicts good adjustment, less pathology, better grades, and interpersonal success, Journal of personality 72(2), 271-324.
Vasbø, K.B., Silseth, K. 6 Erstad, O. (2013). Being a Learner Using Social Media in School: The Case of Space2cre8, Scandinavian Journal of Educational Research, DOI:10.1080/00313831.2013.773555
Vavik, L. & Salomon, G. (2015). Twenty First Century skills vs. disciplinary studies? In Y. Rosen, S. Ferrara and M. Mosharraf (eds.) Handbook of Research on Technology Tools for Real-World Skill Development, Cambridge: MIT press
Vavik, L., Andersland, S., Arnesen, T. E., Arnesen, T., Espeland, M., Flatøy, I. & Tuset, G. A. (2010). Skolefagsundersøkelsen 2009: Utdanning, skolefag og teknologi.HSH-rapport 2010/1. Høgskolen Stord/ Haugesund, Stord
Zimmerman, B.J. & Schunk,D.H. (2011). Self-regulated learning and Performance. An introduction and an Overview. In Zimmerman, B. J., & Schunk, D. H. (Eds.). (2011). Handbook of self-regulation of learning and performance. London: Taylor & Francis. (pp. 1-12)
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