# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### How Do Researchers Combine Audio Features with Other Data Sources? (RQ3)  
Audio features, while rich in information, are often insufficient to capture the full complexity of classroom interactions on their own. Educational settings are inherently multimodal, involving not only spoken language but also gestures, visual cues, physiolog \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature Type** & **Description** \\ \hline
**Acoustic Features** & These capture low-level acoustic properties of audio (e.g., timbre, pitch, intensity, and MFCC). \\ \hline
**Spectral Features** & Includes Mel-frequency cepstral coefficients (MFCC), filter bank energies (Fbank), formant frequencies, spectral entropy, and spectral centroid. These characterize the frequency content of the audio signal. \\ \hline
**Prosodic Features** & Encompasses pitch (fundamental frequency), energy/amplitude (volume), and intensity (decibels), which reveal emphasis, speaking style, or emotional cues. \\ \hline
**Time-Domain/Statistical Features** & Covers zero-crossing rate (ZCR), speech speed/rate (words per minute at the acoustic level), and higher-order statistics (e.g., skewness and kurtosis) of the waveform. \\ \hline
**Chroma** & Represents the intensity of each of the 12 distinct pitch classes in music/speech, useful for tonal or harmonic analysis. \\ \hline
**Diarization Features** & These focus on identifying “who spoke when,” measuring how speech is distributed among individuals, and capturing dynamics of turn-taking. \\ \hline
**Turn-Taking and Number of Turns** & Measures each change of speaker or turn in the conversation (e.g., turn counts, very short utterances, and participant order). \\ \hline
**Speaking Time/Talk Ratio** & Quantifies how long each individual (or group) speaks, useful for comparing teacher vs. student speech. \\ \hline
**Speaker Identification/Uniqueness** & Detects how many distinct voices appear and how often each participant speaks. \\ \hline
**Silence Detection** & Tracks periods of no speech (silent pauses, pause duration, and silence ratio), which can indicate reflection or inactivity. \\ \hline
**Participation Equality/Participant Ratio** & Reflects whether speech is evenly distributed or dominated by a single speaker. \\ \hline
**Speech Overlap/Interruptions** & Monitors when multiple speakers talk simultaneously, showing interaction flow. \\ \hline
**Direction of Arrival** & Locates the position of a speaker in the physical environment, used in multi-microphone setups. \\ \hline
**NLP Features** & These derive from textual representations of speech (i.e., after transcription) and capture linguistic, semantic, or conversational structures. \\ \hline
**Transcription-Based Lexical Features** & Direct use of transcribed text (including raw word tokens, word counts, and words per minute). \\ \hline
**Keyword/Key-Phrase Detection** & Identifies specific terms or question stems (e.g., “why” and “how” domain-related keywords). \\ \hline
**POS Tagging and Grammatical Analysis** & Uses part-of-speech tags, syntactic dependencies, named entities, or discourse relations. \\ \hline
**N-grams, TF-IDF, and Emberddings** & Captures local word sequences (n-grams), term-frequency distributions (TF-IDF), or semantic overlap (embedding-based comparisons and pointwise [PERSON]–[PERSON] divergence). \\ \hline
**Semantic/Pedagogical Indicators** & Focuses on features like “teacher uptake of student ideas”, sentiment or emotion in text, and question authenticity. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of the main features identified in the literature for acoustic, diarization, and natural language processing (NLP) approaches (RQ2). ical signals, and contextual data. To address this, researchers frequently integrate audio with other data sources, a practice widely recognized under the umbrella of multimodal learning analytics (MMLA) [11; 77].