Mastery Guided Non-parametric Clustering to Scale-up Strategy Prediction
Abstract: Predicting the strategy (sequence of concepts) that a student is likely to use in problem-solving helps Adaptive Instructional Systems (AISs) better adapt themselves to different types of learners based on their learning abilities. This can lead to a more dynamic, engaging, and personalized experience for students. To scale up training a prediction model (such as LSTMs) over large-scale education datasets, we develop a non-parametric approach to cluster symmetric instances in the data. Specifically, we learn a representation based on Node2Vec that encodes symmetries over mastery or skill level since, to solve a problem, it is natural that a student's strategy is likely to involve concepts in which they have gained mastery. Using this representation, we use DP-Means to group symmetric instances through a coarse-to-fine refinement of the clusters. We apply our model to learn strategies for Math learning from large-scale datasets from MATHia, a leading AIS for middle-school math learning. Our results illustrate that our approach can consistently achieve high accuracy using a small sample that is representative of the full dataset. Further, we show that this approach helps us learn strategies with high accuracy for students at different skill levels, i.e., leveraging symmetries improves fairness in the prediction model.
- Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. CoRR, abs/2104.13478.
- Corbett, A. T. 2001. Cognitive Computer Tutors: Solving the Two-Sigma Problem. In Proceedings of the 8th International Conference on User Modeling 2001, 137–147.
- Node2vec: Scalable Feature Learning for Networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 855–864.
- Long Short-Term Memory. Neural Computation, 9(8): 1735–1780.
- Processing Megapixel Images with Deep Attention-Sampling Models. In ICML, volume 97 of Proceedings of Machine Learning Research, 3282–3291. PMLR.
- The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning. Cogn. Sci., 36: 757–798.
- Revisiting k-means: New Algorithms via Bayesian Nonparametrics. In Proceedings of the 29th International Conference on Machine Learning.
- Distributed Representations of Words and Phrases and their Compositionality. In Neural Information Processing Systems, 3111–3119.
- Deep Double Descent: Where Bigger Models and More Data Hurt. In ICLR.
- Deep Knowledge Tracing. In Advances in Neural Information Processing Systems, volume 28.
- Identifying Strategies in Student Problem Solving. Design Recommendations for Intelligent Tutoring Systems, 7: 59–70.
- Recent Advances in Conversational Intelligent Tutoring Systems. AI Magazine, 34(3): 42–54.
- Student Strategy Prediction using a Neuro-Symbolic Approach. In Proceedings of the 14th International Educational Data Mining Conference (EDM 21).
- Identification of common molecular subsequences. Journal of Molecular Biology, 147(1): 195–197.
- DataShop: A Data Repository and Analysis Service for the Learning Science Community. In AIED, volume 6738, 628.
- Attention is All you Need. In NIPS, 5998–6008.
- Large-Batch Training for LSTM and Beyond. In International Conference for High Performance Computing, Networking, Storage and Analysis.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.