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Advanced second language performance and influencing factors from the perspective of feature reassembly

A meta-analysis

ORCiD logo with link | Shantou University | Peking University
ORCiD logo with link | Peking University
Published online: 23 October 2026
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https://doi.org/10.1075/lab.25101.hu
Abstract
This meta-analysis examines advanced second language (L2) performance from the perspective of feature reassembly, investigating how reassembly requirements, overtness, and methodological factors influence acquisition difficulty. We synthesized 25 empirical studies yielding 38 effect sizes (N = 1,306). Results show no statistically significant difference (g = −0.092, p = .305) between advanced L2 learners and native speakers. Reassembly requirements do not directly modulate difficulty, but task type does: while learners achieve native-like performance in Form-Focused tasks (g = 0.144, p = .348), a non-significant negative trend is observed in Appropriateness (g = −0.323, p = .200), and significant deficits emerge in Meaning-Mapping tasks (g = −0.679, p = .003). Furthermore, exploratory analyses reveal the transition from L1 overt to L2 non-overt features remains the sole path exhibiting significant deficits (p = .033). We interpret these results through a multi-dimensional framework involving interface-level complexity, communicative competence, typological distance, and the cognitive costs of feature recognition and inhibition.
Article outline
  • 1.Introduction
  • 2.Literature review
    • 2.1Advanced learners and feature reassembly capabilities
    • 2.2Reassembly requirements and acquisition difficulty
    • 2.3The impact of interface on acquisition difficulty
    • 2.4Overt and non-overt features on acquisition difficulty
    • 2.5Research gaps and the present study
  • 3.Research methods
    • 3.1Literature search
    • 3.2Inclusion criteria
    • 3.3Literature coding
    • 3.4Effect size calculation
  • 4.Results
    • 4.1Main effects
    • 4.2Moderator effects
  • 5.Discussion
    • 5.1The L2 advanced performance under feature reassembly hypothesis
    • 5.2Reassembly requirements and typology in acquisition complexity
    • 5.3Feature overtness and acquisition accessibility
    • 5.4Implications for future research
  • 6.Conclusion
  • Data availability
  • CRediT author contributions
  • Use of generative AI
  • References
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