# Improving prediction of students' performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources

arXiv: https://arxiv.org/abs/2403.07194 · HTML: https://mlsanity.com/paper/2403.07194/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 47 references that are in the map.
- First posted on arXiv 10 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Multi-source and multimodal data fusion for predicting academic performance in blended learning university courses](https://mlsanity.com/paper/2403.05552/) (2024)

## Built on by (most cited first, 6 of 6)

- [Time Series Analysis for Education: Methods, Applications, and Future Directions](https://mlsanity.com/paper/2408.13960/) (2024)
- [Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review](https://mlsanity.com/paper/2408.14491/) (2024)
- [Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review](https://mlsanity.com/paper/2509.07742/) (2025)
- [From Complexity to Parsimony: Integrating Latent Class Analysis to Uncover Multimodal Learning Patterns in Collaborative Learning](https://mlsanity.com/paper/2411.15590/) (2024)
- [Multimodal Fine-grained Reasoning for Post Quality Evaluation](https://mlsanity.com/paper/2507.17934/) (2025)
- [Archetypes or ability? Clustering for modelling student mathematical competence](https://mlsanity.com/paper/2607.26063/) (2026)

## Abstract (arXiv)

The aim of this study was to predict university students' learning performance using different sources of data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from face recording videos, interaction zones from eye tracking, and test performance from final knowledge evaluation. Our objective was to test whether the prediction could be improved by using attribute selection and classification ensembles. We carried out three experiments by applying six classification algorithms to numerical and discretized preprocessed multimodal data. The results show that the best predictions were produced using ensembles and selecting the best attributes approach with numerical data.