Diagnostic Questions: The NeurIPS 2020 Education Challenge.

地球仪 课程 质量(理念) 竞赛(生物学) 个性化学习 计算机科学 数学教育 数据科学 心理学 医学教育 教学方法 教育学 医学 开放式学习 合作学习 神经科学 生物 生态学 认识论 哲学
作者
Zichao Wang,Angus Lamb,Evgeny Saveliev,Pashmina Cameron,Yordan Zaykov,José Miguel Hernández-Lobato,Richard E. Turner,Richard G. Baraniuk,Craig Barton,Simon Peyton Jones,Simon Woodhead,Cheng Zhang
出处
期刊:Cornell University - arXiv 被引量:28
摘要

Digital technologies are becoming increasingly prevalent in education, enabling personalized, high quality education resources to be accessible by students across the world. Importantly, among these resources are diagnostic questions: the answers that the students give to these questions reveal key information about the specific nature of misconceptions that the students may hold. Analyzing the massive quantities of data stemming from students' interactions with these diagnostic questions can help us more accurately understand the students' learning status and thus allow us to automate learning curriculum recommendations. In this competition, participants will focus on the students' answer records to these multiple-choice diagnostic questions, with the aim of 1) accurately predicting which answers the students provide; 2) accurately predicting which questions have high quality; and 3) determining a personalized sequence of questions for each student that best predicts the student's answers. These tasks closely mimic the goals of a real-world educational platform and are highly representative of the educational challenges faced today. We provide over 20 million examples of students' answers to mathematics questions from Eedi, a leading educational platform which thousands of students interact with daily around the globe. Participants to this competition have a chance to make a lasting, real-world impact on the quality of personalized education for millions of students across the world.

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