# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 4 Discussion of Findings and Implications
### Data Availability and Privacy Constraints  
One of the most striking patterns across the reviewed literature is the widespread use of locally collected, non-public datasets, often recorded in specific classrooms or institutions to support experimental prototypes [105]. While this is understandable given the logistical and ethical complexities of educational data collection, it has resulted in a highly fragmented research landscape, particularly when it comes to audio and multimodal data (RQ2 and RQ3). There are currently no widely adopted standards for how audio data should be recorded in educational settings. Some studies use a single microphone placed centrally in the classroom; others distribute multiple microphones across the room, and some attach individual recorders to each participant. These decisions, driven by convenience, resources, or technical constraints, profoundly affect the quality and type of features that can be extracted, such as signal-to-noise ratios, speaker separability for diarization, or prosodic fidelity (RQ2). As a consequence, models trained under one recording setup may struggle to generalize to another (RQ4), limiting the applicability of proposed solutions. This issue is further compounded by the near-total absence of dataset publication. Very few studies share their recordings and even fewer offer access to accompanying metadata or extracted features. As a result, it is almost impossible to replicate findings or benchmark new methods on common grounds. This problem is especially critical in a field that increasingly relies on complex AI models whose performance can be sensitive to minor variations in data distribution. Notably absent from most papers is a structured consideration of privacy. Across the 82 reviewed studies, explicit discussions of data protection, anonymization, or ethical frameworks were rare despite the sensitive nature of the data involved. This omission is particularly striking given the frequent use of multimodal inputs (e.g., audio, transcriptions, and video), often without an accompanying explanation of how personally identifiable information is safeguarded. Although some studies propose using anonymized diarization features to preserve privacy [26], most treat privacy as an implicit assumption, not a methodological constraint. However, this invisibility of privacy does not make the problem disappear. Transcripts can expose personal opinions or sensitive content; diarization can link speech to individuals; video can reveal faces and physical environments. Longitudinal studies (e.g., RQ5) raise the stakes even further by enabling behavioral tracking over time. In this context, informed consent is necessary but insufficient. Compliance with data protection frameworks like the General Data Protection Regulation (GDPR) also requires data minimization, purpose limitation, and the right to erasure. Voice is a biometric identifier protected under GDPR Articles 4 and 9, and FERPA treats student speech as personally identifiable information; consequently, large repositories of raw classroom audio cannot be shared across institutions without extensive anonymization or legal agreements. A few notable exceptions do exist. For instance, the TalkMoves dataset [106] provides access to annotated classroom transcriptions focused on mathematics instruction in K-12 settings. While it does not include raw audio, thus limiting its use for acoustic or diarization analysis, it represents an important step toward sharing structured, educational data in a privacy-conscious way. However, such efforts remain isolated and domain-specific. Given these constraints, the field must find a middle ground between analytical ambition and ethical responsibility [107]. Directly sharing raw classroom audio or video may be unfeasible, but a compelling alternative lies in the standardized extraction and anonymized release of feature sets.