Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling
Authors: Shenbao Yu, Yinghui Pan, Yifeng Zeng, Prashant Doshi, Guoquan Liu, Kim-Leng Poh, Mingwei Lin
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models. |
| Researcher Affiliation | Academia | 1 College of Computer and Cyber Security, Fujian Normal University, China 2 National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, China 3 Department of Computer and Information Sciences, Northumbria University, UK 4 Intelligent Thought and Action Lab, School of Computing, University of Georgia, USA 5 Financial Technology Research Institute, Fudan University, China 6 College of Design and Engineering, National University of Singapore, Singapore |
| Pseudocode | Yes | Algorithm 1 PG-BCD+Lipschitz |
| Open Source Code | Yes | Our code is available at https://github.com/Shenbao Yu/AE-NMCF. |
| Open Datasets | Yes | We use real-world students response data with different sparsities and knowledge-exercise relations, which are from diversified academic subjects, including (a) Math (Frc Sub, Junyi-s, and Quanlang-s), (b) Biology (SLP-Bio-s), (c) History (SLP-His-s), and (d) English (SLP-Eng). Frc Sub comprises of the fraction subtraction problem scores of 536 middle school students [10]. Junyi-s includes problem logs from an e-learning website based on the open-source code released by Khan Academy [34]. The private Quanlang-s data set is collected from mathematical exams given to junior schools supplied by QUANLANG education company. 2 Others include SLP- Bio-s, -His-s, and -Eng, which provide unit test results of K-12 learners compiled by an online learning platform (smart learning partner, SLP) [35]. |
| Dataset Splits | Yes | For each dataset, we reshape the response logs to the scoring matrix and utilize a 80%/20% train/test split. |
| Hardware Specification | Yes | We deploy the competing models using the best publicly available implementation with Python 3.8 on an Ubuntu server with a Core i9-1090K 3.7 GHz and 128 GB memory. |
| Software Dependencies | Yes | We deploy the competing models using the best publicly available implementation with Python 3.8 on an Ubuntu server with a Core i9-1090K 3.7 GHz and 128 GB memory. |
| Experiment Setup | Yes | For AE-NMCF, we set the number of iterations and the stopping threshold ϵ as 500 and 5 to guarantee convergence. The hyperparameters T and γ are set in Section F.6. |