Audio Video Verbal Analysis (AVVA) for Capturing Classroom Dialogues
Background: The classroom discourse analysis has been transformed by the growing use of audio-video multimodal data, which demands analytical methods that balance interpretive depth with computational scalability. Methods: This study introduces the Audio Video Verbal Analysis (AVVA) framework, adapted from the Verbal Analysis method to integrate qualitative interpretation with quantitative modelling. Unlike fully multimodal learning analytics approaches, AVVA focuses on verbatim transcripts with essential interactional modalities. Findings: The framework embeds triangulation as a core design strategy across ten methodological steps, strengthening validity and analytical rigour. A comprehensive validation scheme addresses fundamental challenges in temporal observational research: Phi Ceiling for low-frequency variables (via Base Rate Filtering), estimation uncertainty (via bootstrap confidence intervals), and the Modifiable Temporal Unit Problem, where measured associations depend on observational window size. Four-criterion stability assessment (sign consistency, confidence interval overlap, zero exclusion, magnitude stability) classifies variable pairs into interpretable patterns: grain-invariant, scale-specific, or multi-scale, etc. structures across temporal grain sizes. Its application to 23 hours of classroom recordings illustrates its practical viability and its potential to yield meaningful insights. Contribution: The framework thus provides a scalable pathway for transforming rich classroom discourse into analysable datasets.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Quality Over Quantity? LLM-Based Curation for a Data-Efficient Audio-Video Foundation Model
Integrating audio and visual data for training multimodal foundational models remains challenging. We present Audio-Video Vector Alignment (AVVA), which aligns audiovisual (AV) scene content beyond mere temporal synchron…
AudioCapsContrastive LearningLarge Language ModelRetrieval+1WDMIR: Wavelet-Driven Multimodal Intent Recognition
Multimodal intent recognition (MIR) seeks to accurately interpret user intentions by integrating verbal and non-verbal information across video, audio and text modalities. While existing approaches prioritize text analys…
Intent RecognitionMultimodal Intent RecognitionVideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents
Natural human conversation is full-duplex and audio-visual: people simultaneously speak and listen while continuously interpreting and producing nonverbal cues, such as nods, smiles, and gestures. To support successful h…
Visual Question AnsweringAnalysis of Co-Laughter Gesture Relationship on RGB videos in Dyadic Conversation Contex
The development of virtual agents has enabled human-avatar interactions to become increasingly rich and varied. Moreover, an expressive virtual agent i.e. that mimics the natural expression of emotions, enhances social i…
Motion SynthesisAccommodating Audio Modality in CLIP for Multimodal Processing
Multimodal processing has attracted much attention lately especially with the success of pre-training. However, the exploration has mainly focused on vision-language pre-training, as introducing more modalities can great…
AudioCapsContrastive LearningLanguage ModelingLanguage Modelling+3