# Multi-source and multimodal data fusion for predicting academic performance in blended learning university courses

arXiv: https://arxiv.org/abs/2403.05552 · HTML: https://mlsanity.com/paper/2403.05552/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 8 Feb 2024.

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

- [Blended Learning or E-learning?](https://mlsanity.com/paper/1306.4085/) (2013)

## Built on by (most cited first, 4 of 4)

- [Improving prediction of students' performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources](https://mlsanity.com/paper/2403.07194/) (2024)
- [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)
- [Modeling Behavior Change for Multi-model At-Risk Students Early Prediction (extended version)](https://mlsanity.com/paper/2503.05734/) (2025)

## Abstract (arXiv)

In this paper we applied data fusion approaches for predicting the final academic performance of university students using multiple-source, multimodal data from blended learning environments. We collected and preprocessed data about first-year university students from different sources: theory classes, practical sessions, on-line Moodle sessions, and a final exam. Our objective was to discover which data fusion approach produced the best results using our data. We carried out experiments by applying four different data fusion approaches and six classification algorithms. The results showed that the best predictions were produced using ensembles and selecting the best attributes approach with discretized data. The best prediction models showed us that the level of attention in theory classes, scores in Moodle quizzes, and the level of activity in Moodle forums were the best set of attributes for predicting students' final performance in our courses.