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This competition concerns educational diagnostic questions, which arepedagogically effective, multiple-choice questions (MCQs) whose distractorsembody misconceptions. With a large and ever-increasing number of suchquestions, it becomes overwhelming for teachers to know which questions are thebest ones to use for their students. We thus seek to answer the followingquestion: how can we use data on hundreds of millions of answers to MCQs todrive automatic personalized learning in large-scale learning scenarios wheremanual personalization is infeasible? Success in using MCQ data at scale helpsbuild more intelligent, personalized learning platforms that ultimately improvethe 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 thatmimic real learning scenarios and target various aspects of the above questionin a competition setting at NeurIPS 2020. We report on our NeurIPS competitionin which nearly 400 teams submitted approximately 4000 submissions, withencouragingly diverse and effective approaches to each of our tasks.

Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge