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Developing and Validating the Second Language Buoyancy Scale (L2BS): Evidence from Psychometric and Machine Learning Analyses

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Title: Developing and Validating the Second Language Buoyancy Scale (L2BS): Evidence from Psychometric and Machine Learning Analyses
Language: English
Authors: Kenan Gao (ORCID 0009-0004-7476-9514), Juan Zhang (ORCID 0000-0002-7052-1093), Yihui Wang (ORCID 0000-0002-3221-3313), Wei He (ORCID 0000-0001-7786-2715), Jianhong Mo (ORCID 0000-0003-1481-3099)
Source: International Journal of Assessment Tools in Education. 2026 13(1):330-358.
Availability: International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://dergipark.org.tr/en/pub/ijate
Peer Reviewed: Y
Page Count: 29
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Higher Education
Postsecondary Education
Descriptors: Second Language Learning, Measures (Individuals), Test Construction, Test Validity, Construct Validity, Test Reliability, Universities, Undergraduate Students, Psychometrics, Foreign Countries, Artificial Intelligence, Predictive Validity, Graduate Students, English (Second Language)
Geographic Terms: China
ISSN: 2148-7456
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 = 0.83). For structural validity, EFA on Subset 1 (KMO = 0.826; Bartlett's X[superscript 2](6) = 616.99, p < 0.001) supported a single-factor, four-item solution with loadings > 0.65; CFA on Subset 2 showed good fit (RMSEA = 0.096, CFI = 0.992, TLI = 0.977). Internal consistency was strong (Cronbach's [alpha] = 0.898; McDonald's [omega] = 0.898). Construct validity was supported by AVE = 0.689 and small-to-moderate correlations with academic buoyancy, growth mindset, grit, and conscientiousness. Criterion-related validity was evidenced by hierarchical regressions (incremental variance: [delta]R[superscript 2] = 0.173 for L2 engagement; [delta]R[superscript 2] = 0.160 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.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1496154
Database: ERIC
FullText Text:
  Availability: 0
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  Data: Developing and Validating the Second Language Buoyancy Scale (L2BS): Evidence from Psychometric and Machine Learning Analyses
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  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Kenan+Gao%22&quot;&gt;Kenan Gao&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0009-0004-7476-9514&quot;&gt;0009-0004-7476-9514&lt;/externalLink&gt;)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Juan+Zhang%22&quot;&gt;Juan Zhang&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0000-0002-7052-1093&quot;&gt;0000-0002-7052-1093&lt;/externalLink&gt;)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Yihui+Wang%22&quot;&gt;Yihui Wang&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0000-0002-3221-3313&quot;&gt;0000-0002-3221-3313&lt;/externalLink&gt;)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Wei+He%22&quot;&gt;Wei He&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0000-0001-7786-2715&quot;&gt;0000-0001-7786-2715&lt;/externalLink&gt;)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Jianhong+Mo%22&quot;&gt;Jianhong Mo&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0000-0003-1481-3099&quot;&gt;0000-0003-1481-3099&lt;/externalLink&gt;)
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  Data: International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://dergipark.org.tr/en/pub/ijate
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  Data: 29
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  Data: 2148-7456
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Given that existing academic buoyancy measures do not capture learners&#39; 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 = 0.83). For structural validity, EFA on Subset 1 (KMO = 0.826; Bartlett&#39;s X[superscript 2](6) = 616.99, p &lt; 0.001) supported a single-factor, four-item solution with loadings &gt; 0.65; CFA on Subset 2 showed good fit (RMSEA = 0.096, CFI = 0.992, TLI = 0.977). Internal consistency was strong (Cronbach&#39;s [alpha] = 0.898; McDonald&#39;s [omega] = 0.898). Construct validity was supported by AVE = 0.689 and small-to-moderate correlations with academic buoyancy, growth mindset, grit, and conscientiousness. Criterion-related validity was evidenced by hierarchical regressions (incremental variance: [delta]R[superscript 2] = 0.173 for L2 engagement; [delta]R[superscript 2] = 0.160 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.
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  Data: As Provided
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  Label: Entry Date
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  Data: 2026
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  Data: EJ1496154
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 29
        StartPage: 330
    Subjects:
      – SubjectFull: Second Language Learning
        Type: general
      – SubjectFull: Measures (Individuals)
        Type: general
      – SubjectFull: Test Construction
        Type: general
      – SubjectFull: Test Validity
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      – SubjectFull: Universities
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      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Predictive Validity
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      – SubjectFull: English (Second Language)
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      – SubjectFull: China
        Type: general
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      – TitleFull: Developing and Validating the Second Language Buoyancy Scale (L2BS): Evidence from Psychometric and Machine Learning Analyses
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