# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 1 Introduction
Our review fills this gap by (1) comprehensively disaggregating the different categories of audio features used in educational research, (2) revealing how those metrics are extracted and combined, and (3) exposing methodological and ethical voids, such as limited interpretability and the scarcity of anonymized datasets, that have so far prevented findings from translating into practical solutions. To synthesize the current landscape and expose the structural gaps that motivate this review, we present a conceptual diagram outlining two interconnected but misaligned workflows in the field (Figure 1). The current research loop (in red) captures the dominant, technology-driven approach, where audio features are extracted, processed, and modeled to produce analytical results. While this pipeline has yielded significant technical progress, it often operates in isolation from pedagogical practice. Crucial components, such as model explainability, feedback mechanisms, and, above all, the involvement of educational stakeholders, are frequently overlooked from this loop. In contrast, the educational impact loop (in blue) represents a broader, stakeholder-centered vision in which research findings are made explainable and translated into feedback that informs educational practice. The disconnect between these loops, particularly the absence of pedagogical validation and stakeholder engagement, underscores the need for a critical synthesis of how audio features are currently used in educational contexts and how they could be better aligned with real-world needs. Using audio features in education extends beyond signal processing or speech recognition, as it encompasses the broader challenge of extracting relevant information from audio streams to support computational analysis of educational settings. While previous work has explored trends in data-driven education more generally [8], no existing review has systematically examined how audio-derived data are functionally applied across studies. In this review, we focus on the technological uses of audio features, i.e., how they are applied within computational pipelines to detect, classify, or model phenomena observed in educational contexts. These uses reflect the current technical framing of audio features rather than their pedagogical purpose per se. Our goal is to uncover the functional roles that audio features play in existing research, establishing a typology of use cases that guides the subsequent, more technical questions. This leads to our first question: _What are the main technological uses of audio features in educational research? (RQ1)._  
To comprehensively explore the technical landscape of audio analytics in education, we identify the most commonly used audio features and the methods employed for their extraction. Recent trends have shifted from traditional statistical approaches to AI-driven techniques, which enable the direct use of raw or minimally processed data while maintaining strong performance across diverse tasks [9; 10]. Although these elements are central to audio-based analytics, no prior review has offered a systematic categorization of the features and extraction techniques used in educational contexts. With this in mind, we mapped the audio features found in the literature, classified them, and described them in our second research question: _What are the most common audio features used in educational studies and how are they extracted? (RQ2)._  
Educational data are increasingly being collected from a variety of sources, reflecting the inherently multimodal nature of learning environments. Multimodal learning analytics (MMLAs) have emerged as a research area that seeks to integrate data from diverse modalities (e.g, audio, video, text, and physiological signals) to gain deeper insight into learning processes [11]. Within this context, audio features are rarely used in isolation; they are often combined with other forms of data to capture different dimensions of classroom interaction and learner behavior.