# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### How Do Researchers Combine Audio Features with Other Data Sources? (RQ3)
#### 3.4.4 Extent of Interpretability and xAI Practices  
Overall, most articles focus on improving predictive accuracy or illustrating empirical relationships rather than detailing how each audio feature drives model decisions. Nevertheless, three broad approaches to interpretability appear with varying frequencies:  
1. Inductive (Correlation-Based) Analysis: Several studies employ correlation and regression analyses to explore associations between audio features and educational outcomes. While not true explainability methods in the xAI sense, these inductive approaches are used as proxies to validate whether model decisions align with expected behavioral patterns. For example, [PERSON] et al. [82] correlates audio-based collaboration features with model outcomes to check generalizability, while [PERSON] et al. [98] similarly relies on correlation metrics to validate whether ML models remain robust across different classroom environments. These methods provide initial evidence of construct validity but fall short of revealing how individual predictions are made.
2. Feature Importance: Tree-based ML algorithms such as random forests or gradient-boosted trees enable direct analysis of feature contributions, often through built-in importance metrics. These have been used to highlight which acoustic or linguistic features drive model predictions. For instance, [PERSON] et al. [99] identifies para verbal signals as key markers for detecting teacher questions, and [PERSON] et al. [32] uses feature importance to analyze contributors to perceived classroom climate. In a more advanced case, [PERSON] et al. [29] applies SHAP values to clarify how emotional features contribute to warm or encouraging feedback. While useful, such practices are applied inconsistently across studies and often without methodological transparency or justification for the selected xAI technique.
3. Descriptive Analysis: Some studies enhance interpretability by comparing model outputs with human-annotated ground truth or classroom observations. These comparisons, while not algorithmic explanations, provide qualitative insight into how predictions align with real-world phenomena. For instance, [PERSON] et al. [18] illustrates discrepancies between its regression-tree predictions and human-coded discourse segments, while [PERSON] et al. [19] analyzes how the model compares with the performance of human annotators. These approaches can build trust among end users, particularly educators, by revealing whether the system offers pedagogical value in their reasoning. However, they remain anecdotal and rarely constitute a systematic framework for interpretability.  
Across the reviewed literature, statistical models offer inherent transparency by design, while ML models provide moderate interpretability depending on the availability of feature attribution methods. In contrast, DL approaches tend to sacrifice transparency in favor of performance, with only rare instances incorporating formal xAI tools. This trade-off between predictive power and explainability is a key concern for the deployment of these systems in real educational settings, where understanding the basis of decisions is often as critical as the output itself. In our corpus, only 7 out of 27 deep learning studies (26%) explicitly reported the use of explainability techniques, compared to 17 out of 34 machine learning studies (50%). These figures highlight a significant interpretability gap in the most complex and widely adopted approaches, one that deserves more attention if educational stakeholders are to trust and adopt AI-powered systems in practice.