# Generalisable Methods for Early Prediction in Interactive Simulations for Education

arXiv: https://arxiv.org/abs/2207.01457 · HTML: https://mlsanity.com/paper/2207.01457/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling](https://mlsanity.com/paper/1807.00154/) (2018)

## Built on by (most cited first, 1 of 1)

- [Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential Features](https://mlsanity.com/paper/2505.09287/) (2025)

## Abstract (arXiv)

Interactive simulations allow students to discover the underlying principles of a scientific phenomenon through their own exploration. Unfortunately, students often struggle to learn effectively in these environments. Classifying students' interaction data in the simulations based on their expected performance has the potential to enable adaptive guidance and consequently improve students' learning. Previous research in this field has mainly focused on a-posteriori analyses or investigations limited to one specific predictive model and simulation. In this paper, we investigate the quality and generalisability of models for an early prediction of conceptual understanding based on clickstream data of students across interactive simulations. We first measure the students' conceptual understanding through their in-task performance. Then, we suggest a novel type of features that, starting from clickstream data, encodes both the state of the simulation and the action performed by the student. We finally propose to feed these features into GRU-based models, with and without attention, for prediction. Experiments on two different simulations and with two different populations show that our proposed models outperform shallow learning baselines and better generalise to different learning environments and populations. The inclusion of attention into the model increases interpretability in terms of effective inquiry. The source code is available on Github (https://github.com/epfl-ml4ed/beerslaw-lab.git).