“Exercise difficulty prediction in online education systems,”
Abstract
In Online Education System (OES), it is popular to assess examinee by providing exercises with different difficulty. So Exercise Difficulty Prediction (EDP) is a fundamental task of OES, such as medicine examination and school assignment. In view of the rich semantic information embedded in exercise which contains texts and images, it is hard to represent exercise and predict its difficulty. In this paper, we propose a novel Bayesian inference-based Exercise Difficulty Prediction (BEDP) framework to predict difficulty of visual-textual exercise. In BEDP, given exercises with texts, images and difficulty label, we first apply a pretrained Deep Multimodal Embedding model to obtain the unified multimodal representations of each exercise. Then we propose a Bayesian inference-based Softmax Regression classifier to predict the difficulty of exercise. Variational Inference (VI) is applied for approximately solve probabilistic model with a joint distribution of observations and latent variables. By experimenting on two self-collected dataset, we demonstrate the effectiveness of our proposed framework on predicting the difficulty of exercise, and conduct convergence diagnostics for Bayesian inference.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
With automatic testing systems and large quantities of historical test logs, deep learning has been increasingly used for feature extraction and/or difficulty estimation in QDP.
“With automatic testing systems and ready availability of large quantities of historical test logs, deep learning has been increasingly used for feature extraction (word embeddings, question representations, etc.) and/or difficulty estimation (Fang et al., 2019; Lin et al., 2019; Xue et al., 2020).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Fang et al., 2019
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013) · Wiley Series in Probability and Statistics · doi:10.1002/9781118548387 · Cited by 13,861 · see citing works
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512 · see citing works
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018 · see citing works
Dynamic Routing Between Capsules
Sabour, S., Frosst, N., & Hinton, G. E. (2017) · arXiv · doi:10.48550/arxiv.1710.09829 · Cited by 4,167 · see citing works
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1285773 · Cited by 3,974 · see citing works
A survey of decision tree classifier methodology
Safavian, S. R., & Landgrebe, D. A. (1991) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.97458 · Cited by 3,805 · see citing works
Fundamentals of item response theory
(1992) · Choice Reviews Online · doi:10.5860/choice.29-4185 · Cited by 3,417 · see citing works
Graphical Models, Exponential Families, and Variational Inference
Wainwright, M. J., & Jordan, M. I. (2007) · now publishers, Inc. eBooks · doi:10.1561/9781601981851 · Cited by 3,063 · see citing works
Learning to forget: continual prediction with LSTM
Gers, F. A. (1999) · doi:10.1049/cp:19991218 · Cited by 2,436 · see citing works
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. (2023) · Figshare · doi:10.6084/m9.figshare.5203696.v2 · Cited by 2,088 · see citing works
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. (2017) · Figshare · doi:10.6084/m9.figshare.5203696 · Cited by 2,048 · see citing works
A Generalization of Bayesian Inference
Dempster, A. P. (1968) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1968.tb00722.x · Cited by 2,021 · see citing works
(2001) · Cited by 1,932 · see citing works
A Generalization of Bayesian Inference
Dempster, A. P. (2008) · Springer eBooks · doi:10.1007/978-3-540-44792-4_4 · Cited by 1,715 · see citing works
Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
Smith, A. F. M., & Roberts, G. O. (1993) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1993.tb01466.x · Cited by 1,713 · see citing works
(2003) · Cited by 1,680 · see citing works
Expectation Propagation for approximate Bayesian inferencePreprint
Minka, T. P. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.2294 · Cited by 1,391 · see citing works
A family of algorithms for approximate bayesian inference
Minka, T. P., & Picard, R. W. (2001) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 877 · see citing works
The multilayer perceptron as an approximation to a Bayes optimal discriminant function
Ruck, D. W., Rogers, S. K., Kabrisky, M., Oxley, M. E., & Suter, B. W. (1990) · IEEE Transactions on Neural Networks · doi:10.1109/72.80266 · Cited by 834 · see citing works
Stochastic Pooling for Regularization of Deep Convolutional Neural NetworksPreprint
Zeiler, M. D., & Fergus, R. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3557 · Cited by 782 · see citing works
An MCMC approach to classical estimation
Chernozhukov, V., & HONG, H. P. (2003) · Journal of Econometrics · doi:10.1016/s0304-4076(03)00100-3 · Cited by 726 · see citing works
Automatic differentiation variational inference
Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2016) · arXiv · doi:10.48550/arxiv.1603.00788 · Cited by 515 · see citing works
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and TreePreprint
Lee, C., Gallagher, P. W., & Zhuowen Tu, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.08985 · Cited by 386 · see citing works
MegDet: A Large Mini-Batch Object Detector
Peng, C., Xiao, T., Li, Z., Jiang, Y., Zhang, X., Jia, K., Yu, G., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00647 · Cited by 311 · see citing works
Xu, R., Xiong, C., Chen, W. R., & Corso, J. J. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9512 · Cited by 309 · see citing works
Learning to Answer Questions from Image Using Convolutional Neural Network
Ma, L., Lu, Z., & Li, H. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10442 · Cited by 260 · see citing works
Deep Visual-Semantic Hashing for Cross-Modal Retrieval
Cao, Y., Long, M., Wang, J., Yang, Q., & Yu, P. S. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939812 · Cited by 259 · see citing works
Deep multimodal learning for Audio-Visual Speech Recognition
Mroueh, Y., Marcheret, E., & Goel, V. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178347 · Cited by 226 · see citing works
Mathematical reasoning in calculus textbook exercises
Lithner, J. (2004) · The Journal of Mathematical Behavior · doi:10.1016/j.jmathb.2004.09.003 · Cited by 151 · see citing works
Question Difficulty Prediction for READING Problems in Standard Tests
Huang, Z., Liu, Q., Chen, E., Zhao, H., Gao, M., Wei, S., Su, Y., & Hu, G. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10740 · Cited by 118 · see citing works
Automatic Variational Inference in StanPreprint
Kucukelbir, A., Ranganath, R., Gelman, A., & Blei, D. M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03431 · Cited by 115 · see citing works
Boopathiraj, C., & Chellamani, K. (2013) · International Journal Of Social Sciences & Interdisciplinary Research · Cited by 113 · see citing works
An Optimization View on Dynamic Routing Between Capsules
Wang, D., & Liu, Q. (2018) · International Conference on Learning Representations · Cited by 110 · see citing works
General Knowledge Embedded Image Representation Learning
Cui, P., Liu, S., & Zhu, W. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2724843 · Cited by 78 · see citing works
Finding Similar Exercises in Online Education Systems
Liu, Q., Huang, Z., Huang, Z., Liu, C., Chen, E., Su, Y., & Hu, G. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219960 · Cited by 76 · see citing works
Boosting Variational InferencePreprint
Guo, F., Wang, X., Fan, K., Broderick, T., & Dunson, D. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05559 · Cited by 41 · see citing works
Question Difficulty Estimation in Community Question Answering Services
Liu, J., Wang, Q., Lin, C.-Y., & Hon, H.-W. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1009 · Cited by 34 · see citing works
Student Modelling by Case Based Reasoning
Shiri, M. E., Aïmeur, A. E., & Frasson, C. (1998) · Lecture notes in computer science · doi:10.1007/3-540-68716-5_45 · Cited by 23 · see citing works
Exam Question Recommender System
Hage, H., & Aı̈meur, E. (2005) · Cited by 11 · see citing works
MT-MCD: A Multi-task Cognitive Diagnosis Framework for Student Assessment
Zhu, T., Liu, Q., Huang, Z., Chen, E., Lian, D., Su, Y., & Hu, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91458-9_19 · Cited by 10 · see citing works
Similarity Measure of Test Questions Based on Ontology and VSM
Yu, J. S., Li, D., Hou, J., Ying, Y., & Yang, Z. (2014) · The Open Automation and Control Systems Journal · doi:10.2174/1874444301406010262 · Cited by 5 · see citing works
Brizuela, A., & Montero-Rojas, E. (2014) · RELIEVE - Revista Electrónica de Investigación y Evaluación Educativa · doi:10.7203/relieve.19.2.3149 · Cited by 5 · see citing works
Exercise Difficulty Calibration Based on Student Log Mining
Ravi, G. A., & Sosnovsky, S. (2013) · doi:10.13140/2.1.3878.4647 · Cited by 4 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
1 of the 49 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works
A Survey on Recent Approaches to Question Difficulty Estimation from Text
Benedetto, L., Cremonesi, P., Caines, A., Buttery, P. J., Cappelli, A., Giussani, A., & Turrin, R. (2022) · ACM Computing Surveys · doi:10.1145/3556538 · Cited by 37 · see citing works
Graph neural network-based long method and blob code smell detection
Zhang, M., Jia, J., Capretz, L. F., Hou, X., & Tan, H. (2025) · Science of Computer Programming · doi:10.1016/j.scico.2025.103284 · Cited by 4 · see citing works
Zhang, W., Qu, K., Kang, Z., & Hu, S. (2023) · Applied Sciences · doi:10.3390/app13074226 · Cited by 3 · see citing works
Kim, J., Koo, S., & Lim, H. (2023) · Electronics · doi:10.3390/electronics12194171 · Cited by 3 · see citing works
Knowledge Graph Based Absolute Difficulty Prediction of Multiple Choice Question
Wei, L., & Hao, G. (2024) · International Conference on Electronic Technology and Information Science (ICETIS) · doi:10.1109/icetis61828.2024.10593775 · Cited by 2 · see citing works
Question difficulty estimation via enhanced directional modality association transformer
Kim, B.-M., Park, G.-M., & Park, S.-B. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04988-5
Educational Assignment Sources: Data Collection Challenges
Benghachoua, W., Afifi, N., & Hilal, I. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-68653-5_9
Educational data mining in medical education: a scoping review
Dastani, M., & Kashani, M. (2025) · Journal of Medical Education Development · doi:10.61882/edcj.18.3.118
- Authors
- Fang, J., Zhao, W., & Jia, D.
- Venue
- 2019 International Conference on Data Mining Workshops (ICDMW)
- Publisher
- IEEE
- Published
- 2019-11
- Pages
- 311-317
- ISSN
- 2375-9259 (ISSN-L)
- OpenAlex
- W3000343461
- Cited by
- 9
- References
- 49
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 4 · 2022: 1
- FWCI
- 0.29
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE ... International Conference on Data Mining workshops (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- online education system, Bayesian inference, variational inference, multimodal representation, softmax regression, latent variables, convergence diagnostics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
