  Student diversity, like academic background, learning styles, career and life
goals, ethnicity, age, social and emotional characteristics, course load and
work schedule, offers unique opportunities in education, like learning new
skills, peer mentoring and example setting. But student diversity can be
challenging too as it adds variability in the way in which students learn and
progress over time. A single teaching approach is likely to be ineffective and
result in students not meeting their potential. Automated support could address
limitations of traditional teaching by continuously assessing student learning
and implementing needed interventions. This paper discusses a novel methodology
based on data analytics and Machine Learning to measure and causally diagnose
the progress and shortcomings of student learning, and then utilizes the
insight gained on individuals to optimize learning. Diagnosis pertains to
dynamic diagnostic formative assessment, which aims to uncover the causes of
learning shortcomings. The methodology groups learning difficulties into four
categories: recall from memory, concept adjustment, concept modification, and
problem decomposition into sub-goals (sub-problems) and concept combination.
Data models are predicting the occurrence of each of the four challenge types,
as well as a student's learning trajectory. The models can be used to
automatically create real-time, student-specific interventions (e.g., learning
cues) to address less understood concepts. We envision that the system will
enable new adaptive pedagogical approaches to unleash student learning
potential through customization of the course material to the background,
abilities, situation, and progress of each student; and leveraging
diversity-related learning experiences.

Computers and Society;Machine Learning