# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 4 Discussion of Findings and Implications
### Lack of Pedagogical Interpretation in Analytical Results  
A pervasive limitation across the reviewed literature is the tendency to report quantitative results, such as talk-time distributions, question types, or engagement indices, without translating them into pedagogically actionable recommendations. While these studies offer detailed analytics, they rarely address the practical question that educators face: what does this mean for my teaching? Instead, the interpretive burden is implicitly placed on the teacher, who must infer whether the measured patterns are desirable, problematic, or contextually appropriate. The feedback, even when provided, is often generic and descriptive, lacking the guidance necessary to inform instructional decision-making [108]. This disconnect reveals an epistemological mismatch between the precision of computational models and the situated complexity of educational practice. For example, detecting that a teacher asked few \"authentic questions\" may highlight a pattern, but it does not clarify whether this pattern was pedagogically appropriate for that lesson's objectives, student level, or classroom culture. In other cases, studies flag high or low teacher talk-time yet offer no interpretive baseline against which to judge these values. As a result, many outputs remain technically sophisticated but pedagogically inert, becoming precise measurements with unclear implications [109]. In many areas of educational analytics, advanced models have already outperformed classical approaches. For instance, [PERSON], et al. [110] demonstrated that a model based on neural networks trained on clickstream data from a virtual learning environment achieved higher accuracy in predicting at-risk students than traditional logistic regression. This suggests that adopting more complex architectures to learn hierarchical audio representations directly, rather than relying on manually engineered features, could similarly yield more robust and generalizable insights in classroom audio analytics. Furthermore, integrating analytics within established formative assessment models [111] can transform raw metrics into actionable guidance. For example, if audio analytics reveal that students speak less than 10% of class time, a teacher can use that evidence to adjust instruction in real time, perhaps by posing more open-ended questions or incorporating think-pair-share activities to boost engagement. By aligning audio-derived insights with the feedback-for-learning cycle (gather evidence, interpret, adjust instruction, and gather new evidence), educators receive concrete steps, rather than abstract data, to enhance teaching and learning outcomes. To bridge this gap, a more integrated research design that includes the active participation of pedagogical experts from the outset is required. These experts can help determine which features are educationally meaningful and how they should be interpreted within varied classroom contexts [112]. Moreover, their input is essential for translating numeric results into normative statements: not just what is happening, but whether it should be happening, and if not, what might be done differently, as reflected in approaches such as design-based research and human-centered learning analytics [113]. Generative AI offers a promising complement in this regard. When fine-tuned on domain-specific corpora, including classroom recordings, transcriptions, and expert commentary, LLMs could support teachers by delivering conversational, context-aware interpretations of feedback, improving what other works already do [114,115]. Instead of receiving abstract metrics or visualizations, teachers could ask questions like \"Is it a problem that I spoke 80% of the time?\" or \"What does it mean that I used mostly funneling questions?\" and receive grounded, nuanced answers. By embedding expert pedagogical reasoning into the model's responses, LLMs could act as a bridge between analytic outputs and instructional sense-making.