EduStudio: towards a unified library for student cognitive modeling

Abstract

Student cognitive modeling is a fundamental task in the intelligence education field. It serves as the basis for various downstream applications, such as student profiling, personalized educational content recommendation, and adaptive testing. Cognitive Diagnosis (CD) and Knowledge Tracing (KT) are two mainstream categories for student cognitive modeling, which measure the cognitive ability from a limited time (e.g., an exam) and the learning ability dynamics over a long period (e.g., learning records from a year), respectively. Recent efforts have been dedicated to the development of open-source code libraries for student cognitive modeling. However, existing libraries often focus on a particular category and overlook the relationships between them. Additionally, these libraries lack sufficient modularization, which hinders reusability. To address these limitations, we have developed a unified PyTorch-based library EduStudio, which unifies CD and KT for student cognitive modeling. The design philosophy of EduStudio is from two folds. From a horizontal perspective, EduStudio employs the modularization that separates the main step pipeline of each algorithm. From a vertical perspective, we use templates with the inheritance style to implement each module. We also provide eco-services of EduStudio, such as the repository that collects resources about student cognitive modeling and the leaderboard that demonstrates comparison among models. Our open-source project is available at the website of edustudio.ai.

Graphical abstract

Keywords

open-source library / student cognitive modeling / intelligence education

Le Wu is currently a professor at the Hefei University of Technology (HFUT), China. She received her PhD degree from the University of Science and Technology of China (USTC), China. Her general area of research interests are data mining, recommender systems, and responsible user modeling. She has published more than 60 papers in referred journals and conferences, such as IEEE TKDE, NIPS, SIGIR, WWW, and AAAI. Dr. Le Wu is the recipient of the Best of SDM 2015 Award, and the Distinguished Dissertation Award from the China Association for Artificial Intelligence (CAAI) 2017

Xiangzhi Chen is currently pursuing a PhD degree at Hefei University of Technology, China. He received the BE degree from Hefei University of Technology, China in 2021. His research interest lies on educational data mining and artificial intelligence for education. He has published articles in international conferences and journals, such as NeurIPS and IEEE TKDE

Fei Liu received her PhD degree at School of Computer Science and Information Engineering in Hefei University of Technology, China in 2023. She is currently a postdoctoral fellow at Hefei University of Technology, China. Her research mainly lies in educational data mining. She has published articles in international conferences and journals, such as SIGKDD, NeurIPS, ACM Transactions on Information Systems (ACM TOIS), IEEE Transactions on Fuzzy Systems (IEEE TFS), IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE TETCI), and Information Fusion

Junsong Xie is currently pursuing a PhD degree at Hefei University of Technology (HFUT), China. He received the master’s degree from University of Science and Technology of China (USTC), China. His major research interest lies on data mining and recommender systems

Chenao Xia is currently pursuing the MS degree with the School of Computer Science and Technology, Hefei University of Technology, China. He received the BE degree from Anhui Normal University, China in 2022. His current research interests include Educational Data Mining and Deep Learning

Zhengtao Tan is currently working towards a Master’s degree at Hefei University of Technology, China. He received his undergraduate degree from Hefei University of Technology, China in 2022. His research interests include cognitive diagnosis and invariant learning

Mi Tian is currently pursuing a graduate degree at Hefei University of Technology, China. She completed her undergraduate studies at Shanxi University of Finance and Economics, China. Her primary research interest lies in the field of Educational Data Mining, such as Cognitive Diagnosis and Computerized Adaptive Testing

Jinglong Li is currently pursuing a master’s degree at Hefei University of Technology, China. He received his bachelor’s degree from South-Central Minzu University, China in 2023. His major research interest lies in education data mining and out-of-distribution generalization

Kun Zhang received a PhD degree in computer science and technology from the University of Science and Technology of China in 2019. He is currently a faculty member at the Hefei University of Technology (HFUT), China. His research interests include Natural Language Understanding and Recommender Systems. He has published several papers in refereed journals and conferences, such as the IEEE TSMC:S, IEEE TKDE, ACM TKDD, AAAI, KDD, ACL, and ICDM. He received the KDD 2018 Best Student Paper Award

Defu Lian received the PhD degree in computer science from the University of Science and Technology of China (USTC), China in 2014. He is currently a professor of the School of Computer Science and Technology, USTC. He has published prolifically in referred journals and conference proceedings, such as ACM Transactions on Information Systems and IEEE Transaction on Knowledge and Data Engineering, IEEE International Conference on Data Mining, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, and ACM International World Wide Web Conferences. His current research interest includes spatial data mining, recommender systems, and learning to hash

Richang Hong is currently a professor at Hefei University of Technology, China. He received the PhD degree from the University of Science and Technology of China, China in 2008. He has published more than 100 publications in the areas of his research interests, which include multimedia question answering, video content analysis, and pattern recognition. He is a member of the Association for Computing Machinery. He was a recipient of the Best Paper award in the ACM Multimedia 2010

Meng Wang is a professor at the Hefei University of Technology, China. He received his BE degree and PhD degree in the Special Class for the Gifted Young and the Department of Electronic Engineering and Information Science from the University of Science and Technology of China (USTC), China in 2003 and 2008, respectively. His current research interests include multimedia content analysis, computer vision, and pattern recognition. He is an associate editor of IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), IEEE Transactions on Multimedia (IEEE TMM), and IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)

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Acknowledgements

This work was supported in part by grants from the National Science and Technology Major Project, China (Grant No. 2021ZD0111802), the National Natural Science Foundation of China (Grant Nos. 72188101, 62406096, and 62376086), and the Fundamental Research Funds for the Central Universities, China (Grant No. JZ2024HGQB0093).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

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2024 The Author(s) 2024. This article is published with open access at link.springer.com and journal.hep.com.cn