Within-student variability in learning experiences, and teachers’ perceptions of students’ task-focus
Main Article Content
Abstract
In order to advance models of educational processes (intraindividual, intensive longitudinal), we propose a model in which we specify state and trait constructs, and an intraindividual variability construct. In our ecological momentary assessment study, we investigated how trait-level and intraindividual variability of students’ learning experiences (intrinsic and extrinsic motivation, task difficulty, effort exertion, help-seeking and competence evaluations) converged with teacher-reported student task-focus. 285 primary school students’ (Years 5 and 6) completed the Learning Experience Questionnaire using handheld computers, on average 13.6 learning episodes during one week (SD = 4.6; Range = 5-29; nepisodes = 3,433), and these were linked with teacher-reports. We defined mean squared successive differences (MSSD) for each indicator. We specified uni- and multivariate models of state, trait and intraindividual variability constructs of students’ learning experiences using multilevel structural equation models (MSEM). Teacher reported task-focus converged with trait constructs (from |r| = .22 to .42), and with intrapersonal variability constructs (from r = -.12 to r = -.36), higher task-focus being associated with less variability. Intraindividual variability formed a higher-order construct, and teachers’ negative wording bias was also associated with variability. Overall, our study provides support for intraindividual variability as a construct in its own right, which has the potential to provide novel insight into students’ learning processes.
Article Details
How to Cite
Malmberg, L.-E., Lim, W. H. T., Tolvanen, A., & Nurmi, J.-E. (2017). Within-student variability in learning experiences, and teachers’ perceptions of students’ task-focus. Frontline Learning Research, 4(5), 62–82. https://doi.org/10.14786/flr.v4i5.227
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References
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Eid, M., & Diener, E. (1999). Intraindividual variability in affect: reliability, validity, and personality correlates. Journal of Personality and Social Psychology, 76, 662-676. doi:10.1037/0022-3514.76.4.662
Eid, M., & Langeheine, R. (2003). Separating stable from variable individuals in longitudinal studies by mixture distribution models. Measurement: Interdisciplinary Research and Perspectives, 1, 179-206. doi:10.1207/S15366359MEA0103_01
Eizenman, D. R., Nesselroade, J. R., Featherman, D. L., & Rowe, J.W. (1997). Intraindividual variability in perceived control in an older sample: The MacArthur Successful Aging studies. Psychology and Aging, 12, 489-502. doi:10.1037/0882-7974.12.3.489
Georgiou, G., Manolitsis, G., Nurmi, J.-E., & Parrila, R. (2010). Does task-focused versus task-avoidance behavior matter for literacy development in an orthographically consistent language? Contemporary Educational Psychology, 35, 1−10. doi:10.1016/j.cedpsych.2009.07.001
Gill, P., & R. Remedios, R. (2013). How should researchers in education operationalise on-task behaviours? Cambridge Journal of Education, 43, 199-222. doi:10.1080/0305764X.2013.767878
Goetz, T., Frenzel, A. C., Stoeger, H., & Hall, N. C. (2010). Antecedents of everyday positive emotions: An experience sampling analysis. Motivation and Emotion, 34, 49-62. doi:10.1007/s11031-009-9152-2
Hamre, B. K., & Pianta, R. C. (2005). Can instructional and emotional support in the first-grade classroom make a difference for children at risk of school failure? Child Development, 76, 949-967. doi:10.1111/j.1467-8624.2005.00889.x
Hattie, J., Biggs, J., & Purdie, N. (1996). Effects of learning skills interventions on student learning: a meta-analysis. Review of Educational Research, 66, 99-136. doi:10.3102/00346543066002099
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77, 81-112. doi:10.3102/003465430298487
Hirvonen, R., Tolvanen, A., Aunola, K., & Nurmi, J.-E. (2012). The developmental dynamics of task-avoidant behavior and math performance in kindergarten and elementary school. Learning and Individual Differences,22, 715-723. doi: 10.1016/j.lindif.2012.05.014
Hooker, K., Nesselroade, D. W., Nesselroade, J. R., & Lerner, R. M. (1987). The structure of intraindividual temperament in the context of mother-child dyads: P-technique factor analyses of short-term change. Developmental Psychology, 23, 332-346. doi:10.1037/0012-1649.23.3.332
Ilies, R., & Judge, T. A. (2002). Understanding the dynamic relationships among personality, mood, and job satisfaction: A field experience sampling study. Organizational Behavior and Human Decision Processes, 89, 1119–1139. doi:10.1016/S0749-5978(02)00018-3
Inkinen, M., Lonka, K., Hakkarainen, K., Muukkonen, H., Litmanen, T., & Salmela-Aro, K. (2013). The interface between core affects and the challenge-skill relationship. Journal of Happiness Studies, 15, 891-913. doi:10.1007/s10902-013-9455-6
Jahng, S., Wood, P. K., & Trull, T. J. (2008). Analysis of affective instability in ecological momentary assessment: indices using successive difference and group comparison via multilevel modeling. Psychological Methods, 13, 354–375. doi:10.1037/a0014173
Kernis, M. H., Grannemann, B. D., & Barclay, L. C. (1989). Stability and level of self-esteem as predictors of anger arousal and hostility. Journal of Personality and Social Psychology, 56, 1013-1022. doi:10.1037/0022-3514.56.6.1013
La Guardia, J. G., Ryan, R. M., Couchman, C. E., & Deci, E. L. (2000). Within-person variation in security of attachment: a self-determination theory perspective on attachment, need fulfillment, and well-being. Journal of Personality and Social Psychology, 79, 367-384. doi:10.1037/0022-3514.79.3.367
Larsen, R. J. (1987). The stability of mood variability: a spectral analytic approach to daily mood assessments. Journal of Personality and Social Psychology, 52, 1195-1204. doi:10.1037/0022-3514.52.6.1195
Lee, W., & Reeve, J. (2012). Teachers’ estimates of their students’ motivation and engagement: being in synch with students. Educational Psychology, 32, 727–747. doi:10.1080/01443410.2012.732385
Li, X., & Hedeker, D. (2012). A three-level mixed-effects location scale model with an application to ecological momentary assessment data. Statistics in Medicine, 31, 3192-3210. doi:10.1002/sim.5393
Malmberg, L.-E., Walls, T., Martin, A. J., Little, T. D., & Lim, W. H. T. (2013). Primary school students' learning experiences of, and self-beliefs about competence, effort, and difficulty: Random effects models. Learning and Individual Differences, 28, 54–65. doi:10.1016/j.lindif.2013.09.007
Malmberg, L.-E., Woolgar, C., & Martin, A. (2013). Quality of measurement of the Learning Experience Questionnaire for Personal Digital Assistants. International Journal of Quantitative Research in Education, 1, 275-296. doi:10.1504/IJQRE.2013.057689
Malmberg, L.-E., Pakarinen, E., Vasalampi, K., & Nurmi, J-E. (2015). Students’ school performance, task-focus, and situation-specific motivation. Learning and Instruction, 39, 158-167. doi:10.1016/j.learninstruc.2015.05.005
Marsh, H. W. (1989). Age and sex effects in multiple dimensions of self-concept: preadolescence to early adulthood. Journal of Educational Psychology, 81, 417-430. doi:10.1037/0022-0663.81.3.417
McConville, C., & Cooper, C. (1997). The temporal stability of mood variability. Personality and Individual Differences, 23, 161-164. doi:10.1016/S0191-8869(97)00013-5
Morin, A. J. S., Marsh, H. W., Nagengast, B., & Scalas, L. F. (2014). Doubly latent multilevel analyses of classroom climate: An illustration. The Journal of Experimental Education, 82, 143-167. doi:10.1080/00220973.2013.769412
Musher-Eizenman, D. R., Nesselroade, J. R., & Schmitz, B. (2002). Perceived control and academic performance: a comparison of high- and low-performing children on within-person change patterns, International Journal of Behavioral Development, 26, 540–547. doi:10.1080/01650250143000517
Muthén, L. K. & Muthén, B. O. (2012). Mplus statistical analysis with latent variables: User’s guide (Version 7). Muthén and Muthén, Los Angeles.
Nett, U. E., Goetz, T., & Hall, N. C. (2011). Coping with boredom in school: An experience sampling perspective. Contemporary Educational Psychology, 36,49–59. doi:10.1016/j.cedpsych.2010.10.003
Nett, U. E., Goetz, T., Hall, N. C., & Frenzel, A. C. (2012). Metacognitive strategies and test performance: An experience sampling analysis of students’ learning behavior, Education Research International. Article ID 958319, 16 pages. [online] http://kops.ub.uni-konstanz.de/handle/urn:nbn:de:bsz:352-206102 (Accessed 12 April 2013).
Nurmi, J.-E., & Aunola, K. (1998). Observer Rating Scale of Achievement Strategies (OSAS). Unpublished measurement instrument. Jyväskylä: University of Jyväskylä, Finland.
Nurmi, J.-E., Hirvonen, R., & Aunola, K. (2008). Motivation and achievement beliefs in elementary school: A holistic approach using longitudinal data. Unterrichtswissenschaft, 36(3), 237–254.
Pianta, R. C., & Hamre, B. K. (2009). Conceptualization, measurement, and improvement of classroom processes: standardized observation can leverage capacity. Educational Researcher, 38, 109-119. doi:10.3102/0013189X09332374
Pianta, R. C., Belsky, J., Vandergrift, N., Hours, R., & Morrison, F. (2008). Classroom effects on children’s achievement trajectories in elementary school. American Educational Research Journal, 45, 365-397. doi:10.3102/0002831207308230
Ram, N., Chow, S.-M., Bowles, R. P., Wang, L., Grimm, K., Fujita, F., & Nesselroade, J. R. (2005). Examining interindividual differences in cyclicity of pleasant and unpleasant affect using spectral analysis and item response modeling. Psychometrika, 70, 773-790. doi:10.1007/s11336-001-1270-5
Reeve, J., Jang, H., Carrell, D., Jeon, S., & Barch, J. (2004). Enhancing students’ engagement by increasing teachers’ autonomy support. Motivation and Emotion, 28, 147-169. doi:10.1023/B:MOEM.0000032312.95499.6f
Roesch, S. C., Aldridge, A. A., Stocking, S. N., Villodas, F., Leung, Q., Bartley, C. E. & Black, L. J. (2010). Multilevel factor analysis and structural equation modeling of daily diary coping data: Modeling trait and state variation. Multivariate Behavioral Research, 45, 767-789. doi:10.1080/00273171.2010.519276
Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, N J: Princeton University Press.
Schmitz, B. (2006). Advantages of studying processes in educational research. Learning and Instruction, 16, 433-449. doi:10.1016/j.learninstruc.2006.09.004
Schmitz, B. (2015). The study of learning processes using time-series analyses [Video file]. Available from http://www.education.ox.ac.uk/network-on-intrapersonal-research-in-education-nire/seminar-1/bernhard-schmitz/
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