The Promise and Pitfalls of Self-report
Open accessAbstract
As a prelude to this special issue on the promise and pitfalls of self-report, this article addresses three issues critical to its current and future use. The development of self-report is framed in Vertical (improvement) and Horizontal (diversification) terms, making clear the role of both paths for continued innovation. The ongoing centrality of research design and analysis in ensuring that self-reported data is employed effectively is reviewed. Finally, the synergistic use of multiple methods is discussed. This article concludes with an overview of the SI's contributions and a summary of the SI's answers to its three central questions: a) In what ways do self-report instruments reflect the conceptualizations of the constructs suggested in theory related to motivation or strategy use? b) How does the use of self-report constrain the analytical choices made with that self-report data? c) How do the interpretations of self-report data influence interpretations of study findings
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The study's reliance on self-reported data carries inherent risks such as recall bias and distortions in subjective perceptions.
“this study primarily relies on self-reported data. While this method holds significant value across multiple research domains, it carries inherent risks such as recall bias and distortions in subjective perceptions (Fryer and Dinsmore, 2020).”
From Exploring the factors influencing college students’ learning satisfaction in generative AI-supported MOOCs learning environment: a learning experience framework perspective (2025) · cites this paper as Fryer and Dinsmore, 2020
In “This SI’s Mission” and “Not compounding self-report error” (pages unavailable), the paper describes concerns about the reliability and validity of self-report and acknowledges its weaknesses. It does not identify recall bias or distorted subjective perceptions as specific risks, so those examples are not supported by our copy. (LLM, conf=0.83)
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Self-report is indispensable to assess students’ learning
Pekrun, R. (2020) · Frontline Learning Research · doi:10.14786/flr.v8i3.637 · Cited by 160 · see citing works
University students' need satisfaction trajectories: A growth mixture analysis
Gillet, N., Morin, A. J. S., Huyghebaert, T., Burger, L., Maillot, A., Poulin, A., & Tricard, E. (2019) · Learning and Instruction · doi:10.1016/j.learninstruc.2017.11.003 · Cited by 57 · see citing works
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- References Bondarev, A., & Greiner, A. (2019). Endogenous growth and structural change through vertical and horizontal innovations. Macroeconomic Dynamics, 23, 52-79. https://doi.org/10.1017/S1365100516001115 · DOI
- Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait- multimethod matrix. Psychological Bulletin, 56, 81-105.
- Chauliac, M; Catrysse, L. ; Gijbels, D. & Donche V. (2020). It is all in the surv-eye: can eye tracking data shed light on the internal consistency in self-report questionnaires on cognitive processing strategies? Frontline Learning Research. 8 (3), 26 -39. https://doi.org/10.14786/flr.v8i3. · DOI
- Chiu, M. H., Liaw, H. L., Yu, Y. R., & Chou, C. C. (2019). Facial micro-expression states as an indicator for conceptual change in students' understanding of air pressure and boiling points. British Journal of Educational Technology, 50, 469-480. https://doi.org/10.1111/bjet.12597 · DOI
- Dingle, G. A., Hodges, J., & Kunde, A. (2016). Tuned In emotion regulation program using music listening: Effectiveness for adolescents in educational settings. Frontiers in Psychology, 7, 859. https://doi.org/10.3389/fpsyg.2016.00859 · DOI
- Dinsmore, D. L. (2017). Towards a dynamic, multidimensional model of strategic processing. Educational Psychology Review, 29, 235-268. https://doi.org/10.1007/s10648-017-9407-5 · DOI
- Dinsmore, D. L., Alexander, P. A., & Loughlin, S. M. (2008). Focusing the conceptual lens on metacognition, self-regulation, and self-regulated learning. Educational Psychology Review, 20, 391- 409. https://doi.org/10.1007/s10648-008-9083-6 · DOI · Paper page
- Durik, A. M. & Jenkins J. S. (2020). Variability in Certainty of Self-Reported Interest: Implications for Theory and Research. Frontline Learning Research. 8 (3) 85-103. https://doi.org/10.14786/flr.v8i3.491 · DOI
- Fyer, L. & Nakao K. (2020). The Future of Survey Self-report: An experiment contrasting Likert, VAS, Slide, and Swipe touch interfaces. Frontline Learning Research, 8 (3),10-25. https://doi.org/10.14786/flr.v8i3.501 · DOI
- Ginns, P., Martin, A. J., & Papworth, B. (2018). Student learning in Australian high schools: Contrasting personological and contextual variables in a longitudinal structural model. Learning and Individual Differences, 64, 83-93. https://doi.org/10.1016/j.lindif.2018.03.007 · DOI
- Godfroid, A., & Spino, L. A. (2015). Reconceptualizing reactivity of think-alouds and eye tracking: Absence of evidence is not evidence of absence. Language Learning, 65, 896-928. https://doi.org/10.1111/lang.12136 · DOI
- Hidi, S. (2016). Revisiting the role of rewards in motivation and learning: Implications of neuroscientific research. Educational Psychology Review, 28(1), 61-93. https://doi.org/10.1007/s10648-015-9307-5 · DOI
- Iaconelli, R. & Wolters C.A. (2020). Insufficient Effort Responding in Surveys Assessing Self-Regulated Learning: Nuisance or Fatal Flaw? Frontline Learning Research. 8 (3) 104 -125. https://doi.org/10.14786/flr.v8i3.521 · DOI
- Lawless, K. A., & Riel, J. (2020). Exploring the utilization of the big data revolution as a methodology for exploring learning strategy in educational environments. In D.L. Dinsmore, L. K. Fryer, & M. M. Parkinson (Eds.), Handbook of strategies and strategic processing, (pp.296-316). New York: Routledge.
- Kline, R. B. (2011). Principles and practices of structural equation modeling (3 ed.). New York: Guilford Press.
- Martin, A.J. (2011). Prescriptive Statements and Educational Practice: What Can Structural Equation Modeling (SEM) Offer? Educational Psychology Review. 23. 235-244. https://doi.org/10.1007/s10648- 011-9160-0 · DOI
- Mayer, R. E. (2017). How can brain research inform academic learning and instruction? Educational Psychology Review, 29(4), 835-846. https://doi.org/10.1007/s10648-016-9391-1 · DOI
- Moeller, J. ;Viljaranta, J.; Kracke, B. & Dietrich, J. (2020). Disentangling objective characteristics of learning situations from subjective perceptions thereof, using an experience sampling method design. Frontline Learning Research, 8(3), 63-84. https://doi.org/10.14786/flr.v8i3.529 · DOI
- Rogiers, A.; Merchie, E. & Van Keer H. (2020). Opening the black box of students' text-learning processes: A process mining perspective. Frontline Learning Research, 8(3), 40 -62. https://doi.org/10.14786/flr.v8i3.527 · DOI
- Van Halem, N., van Klaveren, C., Drachsler H., Schmitz, M., & Cornelisz, I. (2020). Tracking Patterns in Self-Regulated Learning Using Students' Self-Reports and Online Trace Data. Frontline Learning Research, 8(3) 140-163; https://doi.org/10.14786/flr.v8i3.497 · DOI
- Van Meter, P. (2020) Measurement and the Study of Motivation and Strategy Use: Determining If and When Self-report Measures are Appropriate. Frontline Learning Research, 8(3), 174-184. https://doi.org/10.14786/flr.v8i3.631. · DOI
- Veenman, M. V., Van Hout-Wolters, B. H., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning, 1, 3-14. https://doi.org/10.1007/s11409-006-6893-0 · DOI · Paper page
- Vriesema, C.C., & McCaslin, M. (2020) Experience and Meaning in Small-Group Contexts: Fusing Observational and Self-Report Data to Capture Self and Other Dynamics. Frontline Learning Research, 8(3), 126-139. https://doi.org/10.14786/flr.v8i3.493 · DOI
- Winne, P. (2020) A Proposed Remedy for Grievances about Self-Report Methodologies. Frontline Learning Research. 8 (3) 164 -173. https://doi.org/10.14786/flr.v8i3.625 · DOI
- Yuen, A. H., Cheng, M., & Chan, F. H. (2019). Student satisfaction with learning management systems: A growth model of belief and use. British Journal of Educational Technology, 50(5), 2520-2535. https://doi.org/10.1111/bjet.12830 · DOI
- Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25, 82-91. https://doi.org/10.1006/ceps.1999.1016 · DOI · Paper page
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Lv, P., Wang, X., Nie, Y., Wu, R., & Wang, L. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1633686 · Cited by 5 · see citing works
- Authors
- Fryer, L. K., & Dinsmore, D. L.
- Affiliations
- University of Hong Kong (HK)
- University of North Florida (US)
- Venue
- Frontline Learning Research
- Publisher
- EARLI
- Published
- 2020-03-30
- Volume
- 8
- Issue
- 3
- Pages
- 1-9
- ISSN
- 2295-3159 (ISSN-L)
- OpenAlex
- W3014773865
- Cited by
- 56
- References
- 2
- Citations by year
- 2026: 12 · 2025: 17 · 2024: 8 · 2023: 3 · 2022: 2 · 2021: 5 · 2020: 9
- FWCI
- 7.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Frontline Learning Research (publishedVersion, cc-by-nc-nd)
- University of North Florida Digital Commons (University of North Florida) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Education › Educational Research and Methods
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- self-report measures, self-reported data, research design, data analysis, multiple methods, construct measurement, motivation, strategy use
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