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Student diversity, like academic background, learning styles, career and lifegoals, ethnicity, age, social and emotional characteristics, course load andwork schedule, offers unique opportunities in education, like learning newskills, peer mentoring and example setting. But student diversity can bechallenging too as it adds variability in the way in which students learn andprogress over time. A single teaching approach is likely to be ineffective andresult in students not meeting their potential. Automated support could addresslimitations of traditional teaching by continuously assessing student learningand implementing needed interventions. This paper discusses a novel methodologybased on data analytics and Machine Learning to measure and causally diagnosethe progress and shortcomings of student learning, and then utilizes theinsight gained on individuals to optimize learning. Diagnosis pertains todynamic diagnostic formative assessment, which aims to uncover the causes oflearning shortcomings. The methodology groups learning difficulties into fourcategories: recall from memory, concept adjustment, concept modification, andproblem 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 toautomatically create real-time, student-specific interventions (e.g., learningcues) to address less understood concepts. We envision that the system willenable new adaptive pedagogical approaches to unleash student learningpotential through customization of the course material to the background,abilities, situation, and progress of each student; and leveragingdiversity-related learning experiences.

Dynamic Diagnosis of the Progress and Shortcomings of Student Learning using Machine Learning based on Cognitive, Social, and Emotional Features