Many learners rely on headphones during study, lectures, or group activities to manage their sensory environments. Headphones, particularly modern smart and noise-cancelling models, can provide rich streams of data such as ambient noise levels, user volume preferences, listening patterns, microphone input, and contextual metadata (e.g., location of use, duration of listening). These data can be used to infer sensory load, distractions, or overexposure to noise.
For students with sensory sensitivities, environmental noise can significantly hinder concentration, engagement, and collaboration. By leveraging headphone data, it may be possible to monitor sensory environments, detect stress-inducing conditions, and offer adaptive interventions to promote inclusive and effective learning.
Aim:
To investigate how data collected from headphones can be used to understand and support sensory processing in students, enhancing focus, participation, and learning outcomes.
Objectives:
- Data Capture & Processing
- Collect headphone-based data: noise exposure (dB levels), volume levels, duration of listening sessions, soundscape classification (e.g., speech, music, traffic, silence).
- Link headphone data with contextual learning situations (lectures, self-study, campus movement).
- Model Sensory Stress & Engagement
- Explore relationships between environmental audio conditions and reported sensory experiences (e.g., distraction, stress, fatigue).
- Develop AI/ML models to predict when a learner may be experiencing sensory overload or disengagement based on headphone/environmental data.
- Adaptive Support Design
- Prototype headphone-based interventions (e.g., automatic adjustment of noise cancellation, recommending volume breaks, suggesting a quieter location, or playing focus-supporting soundscapes).
- Develop a companion mobile interface for personalised recommendations and feedback.
- Evaluation
- User testing in real-world academic environments (lecture halls, libraries, group study sessions).
- Assess improvements in focus, comfort, and engagement.
- Consider privacy, data ownership, and ethical implications of collecting ambient and usage data.
Methodology:
- Use headphones equipped with noise-level sensing and usage logging (or external SDKs/APIs from headphone manufacturers).
- Collect parallel self-reported sensory data using ecological momentary assessments (short questionnaires during study sessions).
- Apply supervised and unsupervised ML methods (e.g., classification of overload vs. comfort states, clustering of soundscapes).
- Prototype an adaptive support system that responds to detected conditions.
Expected Outcomes:
- Models linking headphone/environmental audio data with sensory processing states.
- A prototype system that provides real-time, headphone-based sensory regulation support.
- Evidence of how managing auditory environments can enhance inclusive participation in academic settings.
- Ethical guidelines for collecting and using headphone-derived data in education.
Learning Context & Inclusion:
This project addresses common sensory challenges such as:
- Noise sensitivity in large lecture halls or crowded study spaces.
- Overexposure to sound leading to fatigue and reduced attention.
- Need for personalised sound environments to maintain focus during study.
Potential supports include adaptive noise cancellation, personalised focus soundscapes, reminders for safe listening, and environment-aware suggestions (e.g., prompting a student to move to a quieter space).