Developing and validating the Second Language Buoyancy Scale (L2BS): Evidence from psychometric and machine learning analyses

Abstract

Given that existing academic buoyancy measures do not capture learners’ everyday capacity to cope with setbacks in the L2 learning, an L2-specific scale is needed to assess second language (L2) buoyancy. This study aimed to develop and validate the Second Language Buoyancy Scale (L2BS). Using convenience sampling, data were collected from 554 university students at two mainland Chinese institutions and randomly split into two equal subsets (n = 277 per subset). Content validity was established via qualitative item generation (17 interviews) and expert review (ICC = .83). For structural validity, EFA on Subset 1 (KMO = .826; Bartlett’s χ²(6) = 616.99, p < .001) supported a single-factor, four-item solution with loadings > .65; CFA on Subset 2 showed good fit (RMSEA = .096, CFI = .992, TLI = .977). Internal consistency was strong (Cronbach’s α = .898; McDonald’s ω = .898). Construct validity was supported by AVE = .689 and small-to-moderate correlations with academic buoyancy, growth mindset, grit, and conscientiousness. Criterion-related validity was evidenced by hierarchical regressions (incremental variance: ΔR² = .173 for L2 engagement; ΔR² = .160 for L2 enjoyment) and machine-learning models (Random Forest/XGBoost/LightGBM), in which L2BS consistently outperformed academic buoyancy (best accuracies: 73.21% for engagement; 64.29% for enjoyment). Overall, L2BS provides a brief, reliable, and valid measure of L2 buoyancy with clear utility for predicting key L2 outcomes such as L2 engagement and L2 enjoyment.

Keywords

• L2 buoyancy
• Academic buoyancy
• Second language acquisition
• Scale development
• Machine learning

References

1. Aiken, L.S., West, S.G., & Reno, R.R. (1991). Multiple regression: Testing and Interpreting Interactions. Sage.
2. Bartlett, M.S. (1954). A note on the multiplying factors for various χ2 approximations. Journal of the Royal Statistical Society. Series B (Methodological), 16(2) 296-298.
3. Bayrami, M., Heshmati, R., Mohammadpour, V., Gholamzadeh, M., Hasanloo, H.O., & Moslemifar, M. (2012). Happiness and willingness to communicate in three attachment styles: a study on college students. Procedia – Social and Behavioral Sciences, 46, 294 298. https://doi.org/10.1016/j.sbspro.2012.05.109
4. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5 32. https://doi.org/10.1023/A:1010933404324
5. Byrne, B.M., Shavelson, R.J., & Muthén, B. (1989). Testing for the equivalence of factor covariance and mean structures: the issue of partial measurement invariance. Psychological Bulletin, 105(3), 456–466. https://doi.org/10.1037/0033-2909.105.3.456
6. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
7. Clark, L.A., & Watson, D. (2016). Constructing validity: Basic issues in objective scale development. In A.E. Kazdin (Ed.), Methodological issues and strategies in clinical research (4th ed., pp. 187–203). American Psychological Association. https://doi.org/10.1037/14805-012
8. Cohen, J., Cohen, P., West, S.G., & Aiken, L.S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.

9. Collie, R.J., Martin, A.J., Malmberg, L.E., Hall, J., & Ginns, P. (2015). Academic buoyancy, student's achievement, and the linking role of control: A cross‐lagged analysis of high school students. British journal of educational psychology, 85(1), 113-130. https://doi.org/10.1111/bjep.12066
10. Collie, R.J., Perry, N.E., & Martin, A.J. (2017). School context and educational system factors impacting educator stress. In T.M. McIntyre, S.E. McIntyre, & D.J. Francis (Eds.), Educator stress: An occupational health perspective (pp. 3–22). Springer International Publishing/Springer Nature. https://doi.org/10.1007/978-3-319-53053-6_1
11. Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555
12. Costa, P.T., & McCrae, R.R. (1992). Normal personality assessment in clinical practice: The NEO Personality Inventory. Psychological Assessment, 4(1), 5 13. https://doi.org/10.1037/1040 3590.4.1.5
13. Datu, J.A.D., & Yang, W. (2018). Psychometric validity and gender invariance of the academic buoyancy scale in the Philippines: A construct validation approach. Journal of Psychoeducational Assessment, 36(3), 278-283. https://doi.org/10.1177/0734282916674423
14. Datu, J.A.D., Yuen, M., & Chen, G. (2017). Development and validation of the Triarchic Model of Grit Scale (TMGS): Evidence from Filipino undergraduate students. Personality and Individual Differences, 114, 198-205. https://doi.org/10.1016/j.paid.2017.04.012
15. Datu, J.A.D., & Zhang, J. (2021). Validating the Chinese version of triarchic model of grit scale in technical–vocational college students. Journal of Psychoeducational Assessment, 39(3), 381-387. https://doi.org/10.1177/0734282920974813
16. Dewaele, J.M., & Alfawzan, M. (2018). Does the effect of enjoyment outweigh that of anxiety in foreign language performance?. Studies in second language learning and teaching, 8(1), 21-45.
17. Dewaele, J.M., & MacIntyre, P.D. (2014). The two faces of Janus? Anxiety and enjoyment in the foreign language classroom. Studies in Second Language Learning and Teaching, 4(2), 237-274.
18. Dörnyei, Z., & Ryan, S. (2015). The psychology of the language learner revisited. Routledge.
19. Dweck, C.S. (2013). Self-theories: Their role in motivation, personality, and development. Psychology press.
20. Farrington, C.A., Roderick, M., Allensworth, E., Nagaoka, J., Keyes, T.S., Johnson, D.W., & Beechum, N.O. (2012). Teaching adolescents to become learners. The role of noncognitive factors in shaping school performance: A critical literature review. University of Chicago Consortium on Chicago School Research.
21. Field, A. (2024). Discovering statistics using IBM SPSS statistics. Sage publications limited.
22. Field, A.P. (2005). Is the meta-analysis of correlation coefficients accurate when population correlations vary?. Psychological Methods, 10(4), 444-467. https://doi.org/10.1037/1082-989X.10.4.444
23. Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: a comparison of four procedures. Internet Research, 29(3), 430-447. https://doi.org/10.1108/IntR-12-2017-0515
24. Fredrickson, B.L. (2001). The role of positive emotions in positive psychology: the broaden-and-build theory of positive emotions. American Psychology, 56 (3), 218-226. https://doi.org/10.1037/0003-066X.56.3.218
25. Fu, L. (2024). Social support in class and learning burnout among Chinese EFL learners in higher education: Are academic buoyancy and class level important? Current Psychology, 43(7), 5789-5803. https://doi.org/10.1007/s12144-023-04778-9
26. Gao, K., Zhang, J., Wang, Y., Wu, C., & He, W. (2025). Development and validation of the L2 grit scale in collective cultural context (L2GSC): Evidence based on machine learning methods. Journal of China Computer-Assisted Language Learning. https://doi.org/10.1515/jccall-2024-0018
27. Granziera, H., Liem, G.A.D., Chong, W.H., Martin, A.J., Collie, R.J., Bishop, M., & Tynan, L. (2022). The role of teachers' instrumental and emotional support in students' academic buoyancy, engagement, and academic skills: A study of high school and elementary school students in different national contexts. Learning and Instruction, 80, Article 101619. https://doi.org/10.1016/j.learninstruc.2022.101619
28. Gu, J., Zhan, P., Liu, J., & Wang, J.-L. (2023). Strength-based parenting and academic buoyancy: a short-term longitudinal chain mediation model. Current Psychology: A Journal for Diverse Perspectives on Diverse Psychological Issues, 43(8), 6753-6760. https://doi.org/10.1007/s12144-023-04892-8
29. Hejazi, E., & Abbasi, F. (2021). The Effect of Perceived Parental Relationships, Teacher-Student Relationship and Personal Best Goals on Academic Buoyancy. Quarterly of Applied Psychology, 15 (2), 179-205. https://doi.org/10.52547/apsy.2021.216011.0
30. Hernández, A., Hidalgo, M.D., Hambleton, R.K., & Gómez Benito, J. (2020). International test commission guidelines for test adaptation: A criterion checklist. Psicothema, 32(3), 390 398. https://doi.org/10.7334/psicothema2019.306
31. Hirvonen, R., Putwain, D.W., Määttä, S., Ahonen, T., & Kiuru, N. (2020). The role of academic buoyancy and emotions in students’ learning‐related expectations and behaviours in primary school. British Journal of Educational Psychology, 90(4), 948-963. https://doi.org/10.1111/bjep.12336
32. Hirvonen, R., Yli-Kivistö, L., Putwain, D.W., Ahonen, T., & Kiuru, N. (2019). School-related stress among sixth-grade students–Associations with academic buoyancy and temperament. Learning and Individual Differences, 70, 100-108. https://doi.org/10.1016/j.lindif.2019.01.012
33. Hoferichter, F., Hirvonen, R., & Kiuru, N. (2021). The development of school well-being in secondary school: High academic buoyancy and supportive class-and school climate as buffers. Learning and Instruction, 71, Article 101377. https://doi.org/10.1016/j.learninstruc.2020.101377
34. Jahedizadeh, S., Ghonsooly, B., & Ghanizadeh, A. (2019). Academic buoyancy in higher education: Developing sustainability in language learning through encouraging buoyant EFL students. Journal of Applied Research in Higher Education, 11(2), 162-177. https://doi.org/10.1108/JARHE-04-2018-0067
35. Jiang, Y. (2020). A study on the multi-level impact of personality traits and organizational innovation situations on employee career growth [Unpublished doctoral dissertation]. University of Science and Technology of China.
36. Kaiser, H.F. (1970). A second generation little jiffy. Psychometrika, 35(4), 401 415. https://doi.org/10.1007/BF02291817
37. Kanyongo, G.Y. (2006). The influence of reliability on four rules for determining the number of components to retain. Journal of Modern Applied Statistical Methods, 5, 332 343. https://doi.org/10.22237/jmasm/1162353960
38. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 3149-3157). Curran Associates Inc. https://dl.acm.org/doi/10.5555/3294996.3295074
39. Kenny, D.A. (2024, October 6). Measuring model fit. http://davidakenny.net/cm/fit.htm
40. Kieffer, K.M. (1998, November 4-6). Orthogonal versus oblique factor rotation: A review of the literature regarding the pros and cons [Paper presentation]. The AAnnual Meeting of the 27th Mid-South Educational Research Association, New Orleans, LA, United States.
41. Kline, P. (2014). An easy guide to factor analysis. Routledge. https://doi.org/10.4324/9781315788135
42. Kline, T.J. (2005). Psychological testing: A practical approach to design and evaluation. Sage publications. https://doi.org/10.4135/9781483385693
43. Ledesma, R.D., & Valero-Mora, P. (2007). Determining the number of factors to retain in EFA: An easy-to-use computer program for carrying out parallel analysis. Practical Assessment, Research, and Evaluation, 12(1), 2.
44. Ledesma, R.D., Valero-Mora, P., & Macbeth, G. (2015). The scree test and the number of factors: a dynamic graphics approach. The Spanish Journal of Psychology, 18, Article e11.
45. Lei, W., Wang, X., Dai, D.Y., Guo, X., Xiang, S., & Hu, W. (2022). Academic self‐efficacy and academic performance among high school students: A moderated mediation model of academic buoyancy and social support. Psychology in the Schools, 59(5), 885 899. https://doi.org/10.1002/pits.22653
46. Li, C., Dewaele, J.M., & Jiang, G. (2020). The complex relationship between classroom emotions and EFL achievement in China. Applied Linguistics Review, 11(3), 485-510.
47. Li, C., Jiang, G., & Dewaele, J.-M. (2018). Understanding Chinese high school students’ foreign language enjoyment: validation of the Chinese version of the foreign language enjoyment scale. System, 76, 183-196. https://doi.org/10.1016/j.system.2018.06.004
48. Li, C., Wei, L., & Cai, J. (2023). Life satisfaction and L2 engagement in adolescents. ELT Journal, 78(2), 149-159. https://doi.org/10.1093/elt/ccad054
49. Lin, Z. (2021). A methodological review of machine learning in applied linguistics. English Language Teaching, 14(1), 74-85.
50. Luo, T., & Derakhshan, A. (2024). Examining the role of classroom climate and teacher-student relationships in EFL students’ perceived learning outcomes: A self-determination theory perspective. Learning and Motivation, 88, Article 102062. https://doi.org/10.1016/j.lmot.2024.102062
51. McDonald, R.P. (1999). Test Theory: A Unified Treatment. Lawrence Erlbaum Associates.
52. MacIntyre, P.D. (2016). So far so good: An overview of positive psychology and its contributions to SLA. Springer International Publishing.
53. Marsh, H.W., Ellis, L.A., Parada, R.H., Richards, G., & Heubeck, B.G. (2005). A short version of the Self Description Questionnaire II: operationalizing criteria for short-form evaluation with new applications of confirmatory factor analyses. Psychological Assessment, 17(1), 81. https://doi.org/10.1037/1040-3590.17.1.81
54. Martin, A.J., Colmar, S.H., Davey, L.A., & Marsh, H.W. (2010). Longitudinal modelling of academic buoyancy and motivation: Do the 5Cs hold up over time? British Journal of Educational Psychology, 80(3), 473-496. https://doi.org/10.1348/000709910x486376
55. Martin, A.J., & Marsh, H.W. (2006). Academic resilience and its psychological and educational correlates: A construct validity approach. Psychology in the Schools, 43(3), 267-281.
56. Martin, A.J., & Marsh, H.W. (2008a). Academic buoyancy: Towards an understanding of students' everyday academic resilience. Journal of School Psychology, 46(1), 53-83.
57. Martin, A.J., & Marsh, H.W. (2008b). Workplace and academic buoyancy: Psychometric assessment and construct validity amongst school personnel and students. Journal of Psychoeducational Assessment, 26(2), 168-184. https://doi.org/10.1177/0734282907313767
58. Martin, A.J., & Marsh, H.W. (2009). Academic resilience and academic buoyancy: Multidimensional and hierarchical conceptual framing of causes, correlates and cognate constructs. Oxford Review of Education, 35(3), 353-370. https://doi.org/10.1080/03054980902934639
59. Mercer, S. (2019). Language Learner Engagement: Setting the Scene. In: Gao, X. (eds) Second Handbook of English Language Teaching. Springer. https://doi.org/10.1007/978-3-030-02899-2_40
60. Meyer, J., Fleckenstein, J., Retelsdorf, J., & Köller, O. (2019). The relationship of personality traits and different measures of domain-specific achievement in upper secondary education. Learning and Individual Differences, 69, 45-59. https://doi.org/10.1016/j.lindif.2018.11.005
61. Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315-341. https://doi.org/10.1007/s10648-006-9029-9
62. Putwain, D.W., & Daly, A.L. (2013). Do clusters of test anxiety and academic buoyancy differentially predict academic performance? Learning and Individual Differences, 27, 157-162.
63. Putwain, D.W., Daly, A.L., Chamberlain, S., & Sadreddini, S. (2015). Academically buoyant students are less anxious about and perform better in high‐stakes examinations. British Journal of Educational Psychology, 85(3), 247-263. https://doi.org/10.1111/bjep.12068
64. Ren, Y., Ai, L., Yang, Y., & Zhu, F. (2025). Examining the influence of aerobic exercise on middle school students’ academic engagement: exploring the role of academic buoyancy. International Journal of Sport and Exercise Psychology, 1-22. https://doi.org/10.1080/1612197X.2024.2437483
65. Ryan, R.M., & Deci, E.L. (2024). Self-determination theory. In Encyclopedia of quality of life and well-being research (pp. 6229-6235). Cham: Springer International Publishing.
66. Rezaii, N., Walker, E., & Wolff, P. (2019). A machine learning approach to predicting psychosis using semantic density and latent content analysis. NPJ Schizophrenia, 5(1), 9. https://doi.org/10.1038/s41537-019-0077-9
67. Rudd, G., Meissel, K., & Meyer, F. (2021). Measuring academic resilience in quantitative research: A systematic review of the literature. Educational Research Review, 34, Article 100402. https://doi.org/10.1016/j.edurev.2021.100402
68. Schmidt, A.F., & Finan, C. (2018). Linear regression and the normality assumption. Journal of Clinical Epidemiology, 98, 146-151. https://doi.org/10.1016/j.jclinepi.2017.12.006
69. Seligman, M.E., & Csikszentmihalyi, M. (2014). Positive psychology: An introduction. In Flow and the foundations of positive psychology: The collected works of Mihaly Csikszentmihalyi (pp. 279-298). Springer Netherlands. https://doi.org/10.1007/978-94-017-9088-8_18
70. Shatte, A.B.R., Hutchinson, D.M. & Teague, S.J. 2019. Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9). 1426-1448.
71. Sudina, E., & Plonsky, L. (2021). Academic perseverance in foreign language learning: An investigation of language‐specific grit and its conceptual correlates. The Modern Language Journal, 105(4), 829-857. https://doi.org/10.1111/modl.12738
72. Teimouri, Y., Plonsky, L., & Tabandeh, F. (2020). L2 grit: Passion and perseverance for second-language learning. Language Teaching Research, 26(5), 893-918.
73. Vrdoljak, G., Kurtović, A., & Lovaković, I. (2018). Personality traits, goal orientations and school achievement. Primenjena Psihologija, 11(3), 325–344. https://doi.org/10.19090/pp.2018.3.325-344
74. Wiboolyasarin, K., Kamonsawad, R., Jinowat, N., & Wiboolyasarin, W. (2022). EFL learners’ preference for corrective feedback strategies in relation to their self-perceived levels of proficiency. English Language Teaching Educational Journal, 5(1), 32-47.
75. Worthington, R.L., & Whittaker, T.A. (2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34(6), 806 838. https://doi.org/10.1177/0011000006288127
76. Yang, S., Azari Noughabi, M., & Jahedizadeh, S. (2024). Modelling the contribution of English language learners’ academic buoyancy and self-efficacy to L2 grit: Evidence from Iran and China. Journal of Multilingual and Multicultural Development, 45(7), 2701 2717. https://doi.org/10.1080/01434632.2022.2062368
77. Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100 1122. https://doi.org/10.1177/1745691617693393
78. Yao, Y., Wang, W., & Yang, X. (2021). Perceptions of the inclusion of automatic writing evaluation in peer assessment on EFL writers’ language mindsets and motivation: A short-term longitudinal study. Assessing Writing, 50, Article 100568. https://doi.org/10.1016/j.asw.2021.100568
79. Yun, S., Hiver, P., & Al-Hoorie, A.H. (2018). Academic buoyancy: exploring learners' everyday resilience in the language classroom. Studies in Second Language Acquisition, 40(4), 805-830. https://doi.org/10.1017/S0272263118000037