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Title

Meta-Analysis of Emotional Influences on Second Language Acquisition: A Positive Psychology and Educational Neuroscience Perspective

Intro

Emotions shape second language acquisition (SLA) by influencing learners’ cognitive processes, such as attention and motivation. Positive emotions, like enjoyment, are thought to enhance learning by broadening cognitive resources (Fredrickson, 2001), while negative emotions, like anxiety, may hinder it by narrowing focus (Eysenck et al., 2007). Recent studies (e.g., Dewaele et al., 2018; Saito et al., 2018) highlight these effects in classroom settings, yet their relative impact and underlying mechanisms remain unclear. This meta-analysis synthesizes quantitative data to examine how positive and negative emotions affect L2 outcomes, integrating positive psychology and educational neuroscience. By using Meta-Analytic Structural Equation Modeling (MASEM), we aim to clarify the pathways through which emotions influence SLA, offering insights for theory and pedagogy.

Hypothesis

Positive emotions have a stronger positive effect on L2 outcomes (e.g., proficiency, comprehensibility) than negative emotions have a negative effect, with neurocognitive processes (e.g., attention, memory) mediating these relationships.

Research Questions

1. What are the effect sizes of positive emotions (e.g., enjoyment, motivation) and negative emotions (e.g., anxiety, stress) on L2 outcomes in classroom settings?

2. Do neurocognitive processes mediate the relationship between emotions and L2 outcomes?

3. How do teacher characteristics (e.g., enthusiasm) influence the effects of positive and negative emotions on L2 outcomes?

Data Extraction

Data will be extracted into a standardized Excel template, including: - Effect sizes (e.g., Pearson’s r, Cohen’s d, β coefficients). - Sample size, learner characteristics (e.g., age, proficiency), and study design (cross-sectional or longitudinal). - Moderators: teacher characteristics (e.g., enthusiasm, from Dewaele et al., 2019), neurocognitive measures (e.g., task performance as proxies for attention).

Quality Assessment

Study quality will be assessed using the Newcastle-Ottawa Scale for observational studies, focusing on sample representativeness, measurement reliability, and statistical reporting. High-quality studies will be weighted more heavily in analyses.

## Data Analysis Framework ### Random-Effects Meta-Analysis -

**Purpose**: Estimate overall effect sizes for positive and negative emotions on L2 outcomes (RQ1). -

**Steps**: 1. Convert effect sizes to a common metric (Cohen’s d) using standard formulas (e.g., d = 2r/√(1-r²)). 2. Pool effect sizes using a random-effects model to account for heterogeneity across studies. 3. Assess heterogeneity with I² and Q statistics. 4. Conduct moderator analyses (e.g., learner proficiency, study design) using meta-regression. - **Tools**: R package *metafor*.

### Meta-Analytic Structural Equation Modeling (MASEM) - **Purpose**: Test mediation by neurocognitive processes and the role of teacher characteristics (RQ2, RQ3). - **Steps**: 1. Pool correlations (e.g., enjoyment-proficiency, anxiety-attention) from studies to create a correlation matrix for variables: Positive Emotions (enjoyment, motivation), Negative Emotions (anxiety, stress), Neurocognitive Processes (attention, memory, proxied by task performance), L2 Outcomes (proficiency, comprehensibility), and Teacher Characteristics (enthusiasm, support). 2. Specify a SEM model in R (*lavaan*): - Measurement model: Define latent variables (e.g., Positive Emotions =~ Enjoyment + Motivation). - Structural model: Test paths (e.g., Positive Emotions → Neurocognitive Processes → L2 Outcomes; Teacher Characteristics → Positive Emotions). 3. Fit the model using maximum likelihood estimation and evaluate fit: CFI (>0.90), RMSEA (