TITLE: 
Student Strategy Prediction using a Neuro-Symbolic Approach

ABSTRACT:
Predicting student problem-solving strategies is a complex problem but one that can significantly impact automated instruction systems since they can adapt or personalize the system to suit the learner. While for small datasets, learning experts may be able to manually analyze data to infer student strategies, for large datasets, this approach is infeasible. We develop a Machine Learning model to predict strategies from student data. While Deep Neural Network (DNN) based methods such as LSTMs can be applied for this task, they often have long convergence times for large datasets and like several other DNN-based methods have the inherent problem of overfitting the data. To address these issues, we develop a Neuro-symbolic approach for strategy prediction, namely a model that combines strengths of symbolic AI (that can encode domain knowledge) with DNNs. Specifically, we encode relationships in the data using Markov Logic and use symmetries among these relationships to train an LSTM more efficiently. In particular, we use an importance sampling approach where we sample the training data such that for clusters/groups of symmetrical instances (instances where the strategies are likely to be symmetric), we only pick representative samples for training the model instead of using the whole group. Further, since some groups may contain more diverse strategies than the others, we adapt the importance weights based on previously observed samples. Through empirical evaluation on the KDD EDM challenge datasets, we show the scalability of our approach.

AUTHORS:
Anup Shakya, Vasile Rus, Deepak Venugopal

NOTE:
Presented in the workshop as part of the ENCORE track. This paper is from EDM 2021 conference. The paper can be accessed at the following link: https://educationaldatamining.org/EDM2021/virtual/static/pdf/EDM21_paper_177.pdf