# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 1 Introduction  
Audio analysis in educational research can be traced back to the early 1960s when analog recording technologies first enabled the capture of classroom interactions [1]. Initially, researchers used tape recorders to document teacher-student communication, relying on manual transcription and qualitative observations to understand the dynamics of teaching and learning. The transition to digital recording in the 1990s brought a new era, as emerging digital signal processing techniques allowed for more systematic, quantitative analyses of speech patterns and interaction modalities. This evolution marked a shift from purely observational studies to data-driven investigations, enabling the systematic application of computational modeling in educational settings. By the early 2000s, as computational power increased, researchers began employing machine learning techniques to extract and analyze audio features more effectively. In the 2010s and beyond, deep learning and sophisticated artificial intelligence algorithms have further refined the process, enabling the detection of nuanced features such as emotional tone, speaker diarization, and linguistic patterns. Audio features have emerged as a pivotal component in enhancing educational experiences, leveraging the rich information embedded within sound to facilitate learning and engagement. In educational contexts, audio features encompass a range of elements such as speech patterns, acoustic signals, and auditory cues that can be analyzed to assess student performance, provide feedback, and tailor instructional strategies. The integration of audio features into learning analytics and educational contexts has opened new opportunities for personalized learning, accessibility, and real-time assessment [2,3]. In this review, we define audio features as the measurable descriptors derived from raw audio recordings that are used to analyze educational interactions. These features may represent different levels of abstraction, with different tools for each task:  
* Low-level acoustic features, such as pitch, intensity, or spectral representations (e.g., MFCCs or spectrograms), are directly extracted from the audio waveform using libraries such as Librosa [4]. * Diarization features, including speaker turn-taking, speaking time, and participant ratios, are obtained by segmenting and labeling who speaks when. For example, PyAnnote [5] is a very common tool to extract diarization information. * Linguistic features derived from automatic transcriptions of speech, such as word usage, syntactic structure, or discourse-level indicators. Here, we must differentiate between extracting the transcription (for example, using Whisper) [6] and analyzing its content (e.g., spaCy) [7]. Despite their varying degrees of abstraction, all these features share a common origin: they are extracted from audio recordings of classroom or educational interactions. They serve as inputs for computational analyses aimed at modeling pedagogical behaviors, classroom climate, or learner engagement. Throughout this review, we use the term 'audio features' to refer collectively to this spectrum of descriptors, regardless of their proximity to the original waveform. Although several reviews discuss multimodal analytics in education, none treat audio as a distinct and detailed data source. Most prior studies group audio under a generic \"sensor\" label or confine it to limited use cases, such as transcription or emotion detection, without examining the full range of acoustic, diarization, or linguistic metrics and their pedagogical implications. As a result, researchers lack a consolidated view of which audio feature types (e.g., prosodic versus syntactic analysis of transcripts) have proven effective for specific tasks, such as real-time feedback or modeling classroom interaction, and which remain challenging.