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Can machines ‘learn’ word meanings just from lots of textual examples? There has been quite a lot of research into this (see Wikipedia intro), all based on the idea that different meanings tend to show up in rather different contexts eg. move the mouse till the cursor … dissect the mouse and extract its DNA … There is a code … Read more

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 … Read more

Students in higher education frequently face challenges with sensory processing that can impact their ability to focus, engage, and collaborate. These challenges include sensory overload (e.g., from noisy lecture halls or crowded group settings), under-stimulation (e.g., fatigue, low arousal), and distraction (e.g., constant notifications). Addressing these issues is essential for fostering inclusive learning environments. Smart … Read more

Mobile phones are ubiquitous in higher education and are already integrated into students’ daily routines. Beyond their role in communication, they provide a rich source of sensor data (e.g., microphone, accelerometer, gyroscope, screen interaction patterns, GPS, app usage). These data streams can act as proxies for environmental conditions (e.g., noise, movement, crowding) and user states … Read more

Students in higher education often encounter challenges in sensory processing that can affect their ability to fully engage in lectures, tutorials, and collaborative learning environments. Sensory overload (e.g., from noise, crowded rooms, or prolonged concentration) or under-stimulation (e.g., lack of movement, low arousal) can hinder focus, participation, and retention of information. Smartwatches provide a unique … Read more