# 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)
#### 3.3.2 Combination of Audio and Contextual Data  
Beyond video, several studies integrate audio features with contextual data to deepen their understanding of educational processes. These contextual signals, ranging from log traces of student activity to physiological or environmental measurements, offer complementary information that enriches audio-based analyses. Log data are some of the most commonly used forms of contextual information. For example, refs. [59,82] combine audio features with student-level editing traces (e.g., number of characters added or deleted) to estimate collaboration quality. Similarly, studies like [83,84] integrate student interaction logs with audio inputs to evaluate the accuracy and usability of teacher dashboards and socially shared regulation. Some studies also incorporate biological or environmental signals. [PERSON] et al. [47] combines audio with physiological indicators such as pupil diameter and blink rate to classify instructional formats. In another approach, [PERSON] et al. [35] use environmental data (e.g., CO\({}_{2}\) levels, noise, temperature, and air pressure) alongside audio to assess lecture quality. Academic data are also used as a secondary context; for instance, [PERSON] et al. [40] integrate students' academic performance with audio-derived group behavior indicators to contextualize responses in audience response systems. The distribution of feature types used across multimodal studies is summarized in Table 4. Video features dominate the landscape, with 13 instances used in modeling tasks and 6 as support information. In contrast, academic, biological, and environmental data appear more sporadically (each only once), reflecting their specialized use cases. Log data, meanwhile, play a more balanced role, contributing to both modeling and contextual enrichment. What Techniques Are Employed to Process Audio Features in Educational Studies, and to What Extent Are These Solutions Interpretable? (RQ4)  
In analyzing how audio features are computationally processed in educational research, we identified three broad methodological categories: statistical analysis, machine learning, and deep learning. These techniques are applied across the full spectrum of audio features discussed in RQ2, including acoustic properties, speaker diarization metrics, and linguistic indicators derived from transcriptions. This section examines both the nature of these techniques and the degree to which they incorporate strategies for interpretability or explainable AI (xAI). While technical details of the models are not revisited here (see [85] for a comprehensive overview), our \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Type** & **Model** & **Support** \\ \hline Academic & 0 & 1 \\ Biological & 1 & 0 \\ Environmental & 1 & 0 \\ Logs & 3 & 2 \\ Video & 13 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Types of non-audio features used in multimodal studies and their role in modeling or support tasks (RQ3). focus lies in categorizing the methodological choices made by researchers and evaluating how transparently these models support pedagogical interpretation. Figure 8 presents an overview of the relationship between each methodological category and its typical level of interpretability. Interpretability was assessed based on whether studies included correlation-based insights, feature importance rankings, or advanced xAI techniques (e.g., SHAP and LIME). As expected, statistical approaches offer the greatest transparency, while deep learning models, though powerful, generally lack interpretive mechanisms. To explore how these methodological choices map to educational purposes, Figure 9 presents the distribution of techniques across the usage categories defined in RQ1. A clear trend emerges: deep learning and machine learning are more frequently applied in domains like classroom dynamics and emotion and behavior analysis, where patterns are abstract or subtle.