# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### What Are the Main Technological Uses of Audio Features in Educational Research? (RQ1)
#### 3.2.3 NLP-Based Features  
Natural language processing (NLP)-based features are often derived from the textual representation of speech. Typically, the first step involves automatically transcribing classroom oral interactions using a speech recognition system (e.g., [64; 65]), although in some cases, manual transcriptions are used [66; 67; 68]. The quality of transcription, whether automated or manual, critically influences the reliability of downstream NLP analyses. Based on these transcripts, linguistic analysis techniques are applied to extract various indicators, such as word frequency, syntactic complexity, question usage, or keyword identification [18; 19; 38; 69]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Features Combination** & **Count** \\ \hline Acoustic & 21 \\ Acoustic; Diarization & 5 \\ Acoustic; NLP & 10 \\ Diarization & 13 \\ Diarization; NLP & 2 \\ NLP & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of papers with every feature category and combination found (RQ2). Many studies focus primarily on examining how teachers formulate questions, provide feedback, or acknowledge student contributions [70; 71]. This emphasis on linguistic dynamics allows for the analysis of teacher support (feedback-focused), as well as aspects of classroom climate and underlying emotions (emotion and behavior) when expressions of encouragement or positive comments are detected [42]. Beyond simple counts of specific words or phrases (e.g., \"I\" and \"We\" interrogative words) [19], some studies adopt more advanced NLP approaches, such as TF-IDF, transformer-based classification, or semantic embeddings, to classify intervention types, identify authentic questions, or map discourse flow [38; 72]. These methodologies enable researchers to detect specific \"pedagogical moves\" (e.g., rephrasing, requests for justification, or corrective feedback). The processing pipeline often begins with an automatic speech recognition system (e.g., Google Speech-to-Text, Otterai, IBM Watson, or Whisper) that generates the transcription [64; 73]. Next, NLP algorithms are applied to extract linguistic features (e.g., keyword counts, sentiment analysis, grammatical tagging, or discourse segmentation). In more complex cases, deep learning models such as BERT are commonly employed for question classification and semantic encoding, while recurrent architectures (e.g., LSTM) are often used for emotion recognition or discourse modeling [58; 74; 75; 76]. Finally, as with acoustic and diarization features, NLP features could also be combined with these previous features, as shown in Table 1. The combination with acoustic features is more common, as diarization information is typically integrated into ASR systems when they identify individual speakers. Examples of this integration include analyzing socially shared regulation in collaborative learning or understanding how teacher interventions affect student motivation [24; 42]. This range of NLP-based strategies provides researchers with tools to quantify participation and interaction (emotion and behavior analysis), as well as to uncover patterns of teacher feedback and their potential effect on student performance (feedback-focused). Furthermore, NLP is the main group of features used in our defined intervention classification category. Given that verbal content is central to most educational exchanges, NLP-based features offer perhaps the most direct lens into instructional intent and pedagogical nuance, explaining their dominant role across multiple use categories. To provide a comprehensive overview, we constructed a Sankey diagram (Figure 6) to visualize how the usage categories defined in RQ1 (e.g., feedback-focused and emotion and behavior analysis) relate to the audio features types employed across studies. The diagram illustrates how studies connect these categories to the primary audio feature groups (acoustic, diarization, and NLP-based) and, subsequently, to specific extracted features, such as MFCC, transcriptions, and participant ratios.