The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Examining a multidimensional model of student motivation and engagement using a construct validation approach
Abstract
Background. This study seeks to examine a multidimensional model of student motivation and engagement using within‐ and between‐network construct validation approaches. Aims. The study tests the first‐ and higher‐order factor structure of the motivation and engagement wheel and its corresponding measurement tool, the Motivation and Engagement Scale – High School (MES‐HS; formerly the Student Motivation and Engagement Scale). Sample. The study draws upon data from 12,237 high school students from 38 Australian high schools. Methods. The hypothesized 11‐factor first‐order structure and the four‐factor higher‐order structure, their relationship with a set of between‐network measures (class participation, enjoyment of school, educational aspirations), factor invariance across gender and year‐level, and the effects of age and gender are examined using confirmatory factor analysis and structural equation modelling. Results. In terms of within‐network validity, (1) the data confirm that the 11‐factor and higher‐order factor models of motivation and engagement are good fitting and (2) multigroup tests showed invariance across gender and year levels. In terms of between‐network validity, (3) correlations with enjoyment of school, class participation and educational aspirations are in the hypothesized directions, and (4) girls reflect a more adaptive pattern of motivation and engagement, and year‐level findings broadly confirm hypotheses that middle high school students seem to reflect a less adaptive pattern of motivation and engagement. Conclusion. The first‐ and higher‐order structures hold direct implications for educational practice and directions for future motivation and engagement research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance
Cheung, G. W., & Rensvold, R. B. (2002) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem0902_5 · Cited by 16,280
Motivational and self-regulated learning components of classroom academic performance.
Pintrich, P. R., & De Groot, E. V. (1990) · Journal of Educational Psychology · doi:10.1037/0022-0663.82.1.33 · Cited by 8,332
An attributional theory of achievement motivation and emotion
Weiner, B. (1985) · Psychological Review · doi:10.1037/0033-295x.92.4.548 · Cited by 7,846
Building a practically useful theory of goal setting and task motivation: A 35-year odyssey.
Locke, E. A., & Latham, G. P. (2002) · American Psychologist · doi:10.1037/0003-066x.57.9.705 · Cited by 7,766
Learned helplessness in humans: Critique and reformulation.
Abramson, L. Y., Seligman, M. E., & Teasdale, J. D. (1978) · Journal of Abnormal Psychology · doi:10.1037//0021-843x.87.1.49 · Cited by 7,121
Self-Efficacy Beliefs in Academic Settings
Pajares, F. (1996) · Review of Educational Research · doi:10.2307/1170653 · Cited by 4,688
Motivational determinants of risk-taking behavior.
Atkinson, J. W. (1957) · Psychological Review · doi:10.1037/h0043445 · Cited by 3,706
Finn, J. D. (1989) · Review of Educational Research · doi:10.2307/1170412 · Cited by 2,702
Expectancy-value theory of achievement motivation: A developmental perspective
Wigfield, A. (1994) · Educational Psychology Review · doi:10.1007/bf02209024 · Cited by 1,305
Weisz, J. R., Weiss, B., Han, S. S., Granger, D. A., & Morton, T. (1995) · Psychological Bulletin · doi:10.1037/0033-2909.117.3.450 · Cited by 884
Cited by
Jingle, Jangle, and Conceptual Haziness: Evolution and Future Directions of the Engagement Construct
Reschly, A. L., & Christenson, S. L. (2012) · Handbook of Research on Student Engagement · doi:10.1007/978-1-4614-2018-7_1 · Cited by 980
Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.05.038 · Cited by 449
Enhancing student motivation and engagement: The effects of a multidimensional intervention
Martin, A. J. (2008) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2006.11.003 · Cited by 293
Self-citationWang, M.-T., Fredricks, J., Ye, F., Hofkens, T., & Linn, J. S. (2019) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000431 · Cited by 214
Beyond ‘doing time’: Investigating the concept of student engagement with feedback
Handley, K., Price, M., & Millar, J. (2011) · Oxford Review of Education · doi:10.1080/03054985.2011.604951 · Cited by 181
Steinmayr, R., Weidinger, A. F., & Wigfield, A. (2018) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2018.02.004 · Cited by 163
Tempelaar, D., Rienties, B., & Nguyen, Q. (2020) · PLOS ONE · doi:10.1371/journal.pone.0233977 · Cited by 137
(2006) · Cited by 126
Student profiling in a dispositional learning analytics application using formative assessment
Tempelaar, D., Rienties, B., Mittelmeier, J., & Nguyen, Q. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2017.08.010 · Cited by 122
Unpacking student engagement in higher education learning analytics: a systematic review
Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00493-y · Cited by 138
Adaptability, engagement, and degree completion: a longitudinal investigation of university students
Holliman, A. J., Martin, A. J., & Collie, R. J. (2018) · Educational Psychology · doi:10.1080/01443410.2018.1426835 · Cited by 89
Self-citationStudent engagement: three models for its investigation
Payne, L. (2019) · Journal of Further and Higher Education · doi:10.1080/0309877x.2017.1391186 · Cited by 55
The development and validation of the Relational Self-esteem Scale
DU, H., KING, R. B., & CHI, P. (2012) · Scandinavian Journal of Psychology · doi:10.1111/j.1467-9450.2012.00946.x · Cited by 52
Xia, Q., Li, W., Yang, Y., Weng, X., & Chiu, T. K. F. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100455 · Cited by 45
A PRISMA review of expectancy-value theory in music contexts
Sin, H. X., Tan, L., & McPherson, G. E. (2022) · Psychology of Music · doi:10.1177/03057356211024344 · Cited by 24
Kong, L., Zhao, M., Huang, W., Zhang, W., & Liu, J. (2025) · BMC Psychology · doi:10.1186/s40359-025-02696-y · Cited by 21
A multi-modal study into students’ timing and learning regulation: time is ticking
Tempelaar, D., Rienties, B., & Nguyen, Q. (2018) · Interactive Technology and Smart Education · doi:10.1108/itse-02-2018-0015 · Cited by 12
Bond, M., Viberg, O., & Bergdahl, N. (2023) · LAK23: 13th International Learning Analytics and Knowledge Conference · doi:10.1145/3576050.3576085 · Cited by 8
Metadata
- Authors
- Martin, A. J.
- Venue
- British Journal of Educational Psychology
- Publisher
- Wiley
- Published
- 2007-06
- Volume
- 77
- Issue
- 2
- Pages
- 413-440
- ISSN
- 0007-0998 (ISSN-L)
- 2044-8279
- OpenAlex
- W2131347595
- Cited by
- 727
- References
- 11
- FWCI
- 13.70
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.