TY - GEN
T1 - Educational Question Mining At Scale
T2 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
AU - Wang, Zichao
AU - Tschiatschek, Sebastian
AU - Woodhead, Simon
AU - Hernández-Lobato, José Miguel
AU - Jones, Simon Peyton
AU - Baraniuk, Richard G.
AU - Zhang, Cheng
N1 - Funding Information:
We thank the anonymous reviewers for their constructive feedback. ZW and RGB are supported by NSF grants 1842378 and 1937134 and by ONR grant N0014-20-1-2534.
Funding Information:
Work done during internship at Microsoft Research Cambridge. Work done at Microsoft Research Cambridge. We thank the anonymous reviewers for their constructive feedback. ZW and RGB are supported by NSF grants 1842378 and 1937134 and by ONR grant N0014-20-1-2534.
Publisher Copyright:
Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved
PY - 2021
Y1 - 2021
N2 - Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students. With large volumes of available questions, it is important to have an automated way to quantify their properties and intelligently select them for students, enabling effective and personalized learning experiences. In this work, we propose a framework for mining insights from educational questions at scale. We utilize the state-of-the-art Bayesian deep learning method, in particular partial variational auto-encoders (p-VAE), to analyze real students' answers to a large collection of questions. Based on p-VAE, we propose two novel metrics that quantify question quality and difficulty, respectively, and a personalized strategy to adaptively select questions for students. We apply our proposed framework to a real-world dataset with tens of thousands of questions and tens of millions of answers from an online education platform. Our framework not only demonstrates promising results in terms of statistical metrics but also obtains highly consistent results with domain experts' evaluation.
AB - Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students. With large volumes of available questions, it is important to have an automated way to quantify their properties and intelligently select them for students, enabling effective and personalized learning experiences. In this work, we propose a framework for mining insights from educational questions at scale. We utilize the state-of-the-art Bayesian deep learning method, in particular partial variational auto-encoders (p-VAE), to analyze real students' answers to a large collection of questions. Based on p-VAE, we propose two novel metrics that quantify question quality and difficulty, respectively, and a personalized strategy to adaptively select questions for students. We apply our proposed framework to a real-world dataset with tens of thousands of questions and tens of millions of answers from an online education platform. Our framework not only demonstrates promising results in terms of statistical metrics but also obtains highly consistent results with domain experts' evaluation.
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M3 - Conference contribution
AN - SCOPUS:85115371050
T3 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
SP - 15669
EP - 15677
BT - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
PB - Association for the Advancement of Artificial Intelligence
Y2 - 2 February 2021 through 9 February 2021
ER -