Modeling the Regulation of Students' Study Behaviors and Motivational Deficits: A Longitudinal Test of Order and Effect
Author(s):
Luke K. Fryer (presenting / submitting) Kaori Nakao (presenting) Paul Ginns Richard Walker
Conference:
ECER 2015
Network:
WERA Focal Meeting
Format:
Paper

Session Information

WERA SES 09 C, International Trends on Motivation for Academic Performance

Paper Session

Time:
2015-09-10
11:00-12:30
Room:
305. [Main]
Chair:
Bee Leng Chua

Contribution

The regulation of students’ studies and the motivations which propel students to study are widely acknowledged as being tightly intertwined (e.g. Pintrich, 1989; Pintrich, 2004; Pintrich & DeGroot, 1990; Pintrich & Zusho, 2002; Pintrich, Zusho, Schiefele, & Pekrun, 2001). The nature and ordering of this relationship is, however, poorly understood. Furthermore, our understanding of this interplay between motivaiton and strategy is almost entirely based on research with students attending Western instutions. The current study aimed to begin to address these issues

In the current study a longitudinal sample is employed to test and then compare two reciprocal models of motivation and strategy and then test the model across the first two years of university. This test was carried out with students engaged in their first and second year at Japanese university. This is a critical transition period from compulsory to optional formal education, which is crucial to students' future. This transition was expected to have an important effect on the development of students’ study strategies and motivations for studying. For the current study, motivation was assessed as students’ deficits in three critical aspects of motivation: value, ability and effort. A deficit perspective was employed due to the non-compulsory nature of the educational context understudy. It is reasonable to assume that students will enter university without critical deficits in these motivations. It is, however, also reasonable to assume that such deficits might arise as students work through their first and then second year at university.  

For the current study, a tripartite model of regulation was employed (Vermunt, 1994, 1998). This model of regulation includes a full continuum of students’ regulation for studying in their individual departments at university: self-regulation, external regulation and lack of regulation.

To assess students’ critical deficits in motivation, the Academic Amotivation Inventory (AAI; Legault, Green-Demers, & Pelletier, 2006) was employed to measure students’ amotivation for studying in their individual department. Based on prior research with the AAI (Fryer, Bovee, & Nakao, 2014) the current study employed three of the four dimensions included in the AAI: Task-valuation, Effort beliefs, and Ability beliefs. 

Method

The current study was undertaken with first-year students’ (n =580) from six departments at one large private university in Western Japan. Students reported their reasons for not studying (lack of…ability belief, value for learning, and effort beliefs) and regulation strategies (self-, external and lack of regulation), three times, nine and seven months apart, at the beginning/end of their first year and after semester one of students’ second year. Structural Equation Modelling was employed for construct validation, model comparison and latent, simultaneous regression. Structural equation modelling was undertaken within Mplus 7.0 ( Muthén & Muthén, 1998-2013), employing the Maximum Likelihood Robust (MLR) estimator. The data set employed for all analyses had less than 2% missing data, which was accounted for by the MLR algorithm (Full Information Maximum Likelihood). Fit for models was measured using the Root Mean Square Error of Approximation (RMSEA) (Browne & Cudeck, 1992), with values < .08 and < .05 held to indicate acceptable and good fit respectively, and the Confirmatory Fit Index (CFI) and Tucker-Lewis Index (TLI) (e.g. Marsh, Balla, & McDonald, 1988) with values > .90 and > .95 held to indicate acceptable and good fit respectively. For the interpretation of structural equation modelling findings, the current study relied on ß coefficient results. The conversion of ß coefficients was undertaken based on Peterson and Brown‘s (2005) recommendations, in line with Hattie’s (2009) guidelines for educational effect sizes. The current study employed three levels of ß weights for describing the effect of independent on dependent variables. For positive effects, a small ß = .05; a moderate ß = .15; and a large ß = .24 and above. For negative effects, a small ß = -.10; a moderate ß = -.20; and a large ß = -.29 and above.

Expected Outcomes

Findings For the current proposal, Time-1 to Time-2 results are presented. Time-3 will be added for the presentation. Prior to longitudinal modelling, a mass Confirmatory Factor Analysis was conducted and found to fit acceptably. Following this initial test of fit and validity, two models were tested. Model 1 positioned students’ reasons for not studying predicting both cross-sectional and future regulation strategies and Model 2 reversed this ordering. Model 1 pursued the logical ordering of why(not) followed by how and was therefore expected to be the most appropriate model for explaining the reciprocal model tested. While both models fit the data acceptably, Model 2 fit best: Model 1, RMSEA = .045 (C.I. 90% .038 ~ .042), CFI = .92, TLI = .91; Model 2, RMSEA = .041 (C.I. 90% .039 ~ .043), CFI = .93, TLI = .92. Model 2 was therefore reported and discussed in this preliminary proposal for presentation. In addition to the latent variables already described, annualized grade point average was included as learning outcome predicted by all latent variables modelled. Preliminary Conclusions and Scientific Significance Key preliminary results: 1) the adaptive role of both external and self-regulation for cross-sectionally reducing deficits motivation; 2) the strongly contrasting negative and positive effects of the same strategies on value related reasons for not studying over a nine month (two semester) gap; 3) the essential role of ability belief related motivations for achievement outcomes; 4) salient effect of gender’s on strategies, motivational deficits and achievement. This paper integrates two models for understanding how and why students study. In doing so it provides a new perspective on students’ university experience and more specifically how the university experience can change students’ study behaviours and critical motivations for studying. It also highlights the diverse role that gender can play within students' studies.

References

References Fryer, L. K., Bovee, H. N., & Nakao, K. (2014). E-learning: Reasons language students don't want to. Computers & Education. doi: 10.1016/j.compedu.2014.01.008 Legault, L., Green-Demers, I., & Pelletier, L. (2006). Why do high school students lack motivation in the classroom? Toward an understanding of academic amotivation and the role of social support. Journal of Educational Psychology, 98, 567-582. doi: 10.1037/0022-0663.98.3.567 Pintrich, P. R. (1989). The dynamic interplay of student motivation and cognition in the college classroom. In C. Ames & M. Maehr (Eds.), Advances in motivation and achievement: Vol. 6. Motivation enhancing environments (pp. 117-160). Greenwich, CT: JAI Pres. Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16, 385-407. doi: 10.1007/s10648-007-9050-7 Pintrich, P. R., & DeGroot, E. V. (1990). Motivational and self-regulated learning components of classroom academic-performance. Journal of Educational Psychology, 82, 33-40. doi: 10.1037/0022-0663.82.1.33 Pintrich, P. R., & Zusho, A. (2002). The develpment of academic self-regulation: The role of cognitive and motivational factors. In A. Wigfield & J. Eccles (Eds.), Developmental of achievement motivation (pp. 149-169). Pintrich, P. R., Zusho, A., Schiefele, U., & Pekrun, R. (2001). Goal orientation and Self-regulated learning in the college classroom: A cross-cultural comparison. In F. Salili, C. Y. Chiu & Y. Y. Hong (Eds.), Student motivation: The culture and the context of learning. (pp. 1-13). New York: Kluwer Academic / Plenum Press. Vermunt, J. D. (1994). Inventory of learning styles (ILS) in higher education. University of Tilburg. Vermunt, J. D. (1998). The regulation of constructive learning processes. British Journal of Educational Psychology, 68, 149-171. doi: 10.1111/j.2044-8279.1998.tb01281.x

Author Information

Luke K. Fryer (presenting / submitting)
Sydney University, Australia
Kaori Nakao (presenting)
Kyushu Sangyo University, Japan
Paul Ginns
Sydney University, Australia
Richard Walker
Sydney University, Australia

Update Modus of this Database

The current conference programme can be browsed in the conference management system (conftool) and, closer to the conference, in the conference app.
This database will be updated with the conference data after ECER. 

Search the ECER Programme

  • Search for keywords and phrases in "Text Search"
  • Restrict in which part of the abstracts to search in "Where to search"
  • Search for authors and in the respective field.
  • For planning your conference attendance, please use the conference app, which will be issued some weeks before the conference and the conference agenda provided in conftool.
  • If you are a session chair, best look up your chairing duties in the conference system (Conftool) or the app.