Special Issue on Signal Processing and Machine Learning for Education and Human Learning at Scale

Aims and Scope
The surge in popularity of Massive Open Online Courses (MOOCs) and other online and blended learning platforms has demonstrated the potential of the Internet for scaling education. While advances in technology have enabled content delivery to massive numbers of students, these platforms remain limited in their ability to provide an effective learning experience for each individual.

Recent advances in machine learning and signal processing offer promising avenues to move beyond this “one size fits all” educational approach. The key is that today’s learning technology platforms can capture big data about learners as they proceed through courses. Examples of learning data include performance on homeworks and exams, click actions made while watching lecture videos or interacting with simulations, the social learning networks formed among the students, and the content posted on discussion forums. Going even further, prototype platforms are being built that use cameras and other sensors to continuously monitor students’ affect and engagement. The large volumes of empirical learning data being collected present novel opportunities to study the process of student learning, to design systems that improve learning at scale by closing the learning feedback loop.