Summary of Scalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data, by Anup Shakya et al.
Scalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data
by Anup Shakya, Vasile Rus, Deepak Venugopal
First submitted to arxiv on: 7 Aug 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers focus on developing an AI-powered system to predict students’ problem-solving strategies in math learning. The goal is to create an Intelligent Tutoring System (ITS) and Adaptive Instructional System (AIS) that can personalize itself based on individual students’ strengths and weaknesses. To achieve this, the authors leverage advances in Machine Learning and AI methods, developing a novel approach called MVec that learns student mastery representations. They then cluster these embeddings using a non-parametric method to identify symmetrical strategies. The strategy prediction model is trained on instances from these clusters to ensure diversity and fairness for students of all skill levels. The researchers test their approach using real-world datasets from MATHia, demonstrating high accuracy and predictive equality for learners at diverse skill levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create a better math learning experience by understanding how students solve problems. It’s like having a special teacher that can adapt to each student’s unique way of thinking! The researchers used machine learning and artificial intelligence to develop a system that can learn from large amounts of data and predict which strategies will work best for individual students. |
Keywords
* Artificial intelligence * Machine learning
