Digital Library

Hybrid agent-based machine learning simulation of a classroom disruption model

• Khulood Alharbi
• Alexandra Cristea

(2024). ECMS 2024, 38th Proceedings
Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2024
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-84-5
ISBN: 978-3-937436-83-8 (CD) Communications of the ECMS Volume 38, Issue 1, June 2024, Cracow, Poland June 4th – June 7th, 2024

https://doi.org/10.7148/2024-0125

Khulood alharbi, Alexandra cristea (2024). Hybrid Agent-Based Machine Learning Simulation of a Classroom Disruption Model, ECMS 2024, Proceedings Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation. doi:10.7148/2024-0125

Agent-Based Models (ABMs) have been used in the field of education as a learning tool. However, the use of ABM as a tool for educational stakeholders is not well-represented, nor machine learning (ML) for ABM. Here we extend our work on a classroom ABM, by developing an ABM & ML hybrid model that simulates classroom disruptive interactions during a school year, and outputs predicted learning outcomes. We use real-life data from a primary school monitoring system that contains 65,385 student records from 2,040 schools across the UK as well as simulated interaction data, to implement linear regression for predictions of math scores, and ABM interactions to update the final score, to reflect the effect of these interactions. We show that this hybrid ABM model outperforms a simple ABM model.