  This competition concerns educational diagnostic questions, which are
pedagogically effective, multiple-choice questions (MCQs) whose distractors
embody misconceptions. With a large and ever-increasing number of such
questions, it becomes overwhelming for teachers to know which questions are the
best ones to use for their students. We thus seek to answer the following
question: how can we use data on hundreds of millions of answers to MCQs to
drive automatic personalized learning in large-scale learning scenarios where
manual personalization is infeasible? Success in using MCQ data at scale helps
build more intelligent, personalized learning platforms that ultimately improve
the quality of education en masse. To this end, we introduce a new,
large-scale, real-world dataset and formulate 4 data mining tasks on MCQs that
mimic real learning scenarios and target various aspects of the above question
in a competition setting at NeurIPS 2020. We report on our NeurIPS competition
in which nearly 400 teams submitted approximately 4000 submissions, with
encouragingly diverse and effective approaches to each of our tasks.
