# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 1 Introduction
These combinations allow researchers to explore richer representations of educational phenomena, though the methods and rationales for such  
Figure 1: Conceptual overview of current and idealized research workflows involving audio features in educational contexts. The red loop represents the dominant technical pipeline, which focuses on extracting and modeling audio features but largely excludes explainability, feedback, and the participation of educational stakeholders. The blue loop reflects a more complete, stakeholder-centered perspective, where analysis leads to actionable outcomes through explainable models and feedback mechanisms. The dashed arrow highlights the lack of pedagogical validation connecting research outputs back to educational use cases. integration vary widely. This leads us to our third research question: _How do researchers combine audio features with other data sources? (RQ3)._  
As audio features become more prevalent in educational research, a key concern is how they are computationally processed. In recent years, there has been a growing reliance on both traditional machine learning algorithms and deep learning models to handle tasks such as classification, clustering, and prediction [12]. These techniques are often applied to features derived from audio, such as speaker diarization outputs and automatic transcriptions, features that, despite being abstracted through additional processing, still originate from the raw audio stream. While these methods can yield strong performance, their complexity raises concerns about interpretability, which is a crucial factor when insights are intended to inform pedagogical decisions. Although explainable AI (xAI) has gained attention in other domains, its adoption within educational audio analytics remains limited. To examine this relationship between performance and transparency, we ask the following: _What techniques are employed to process audio features in educational studies, and to what extent are these solutions interpretable? (RQ4)._  
Finally, we turn to the practical implications of audio-based research in education by examining the extent to which these studies lead to actionable outcomes in real-world settings. Specifically, we explore whether researchers implement mechanisms to provide feedback to participants, such as teachers or students, based on insights derived from audio analysis. Feedback loops are essential for translating research into practice and fostering impact in educational environments [13]. This forms the basis of our final research question: _Which studies provide feedback for participants derived from obtained results? (RQ5)._  
This systematic review synthesizes the literature on the use of audio features in educational contexts, addressing how these features are applied, extracted, and interpreted across studies. The research questions are organized to reflect a gradual progression, from general trends and uses of audio data in education to the specific types of features most frequently extracted and the techniques used to process them. This review not only synthesizes the current state of research on audio-based methods in educational contexts but also advances the field by identifying three core limitations and offering strategic directions to address them:  
* Targeted analysis of audio features. Unlike earlier reviews that broadly categorize audio under \"multimodal\" or \"sensor\" headings, our research focuses specifically on audio as a standalone modality. It offers a focused examination of audio features within educational research. It covers a wide range of them, including low-level acoustic properties, linguistic indicators extracted via NLP, and speaker-based metrics obtained through diarization. As far as we are aware, no previous study has provided a systematic identification, categorization, and definition of the audio features employed in educational settings. * Diagnosis of field-level limitations.