Inclusive education via empathy propagation in schools of students with special education needs

TL;DR

The paper addresses how to reverse segregation of students with SEN by enabling empathy propagation in school networks. It uses a cellular automata framework inspired by Schelling's segregation model, with two-state agents ($SEN=1$, $nonSEN=0$) on a $100\times100$ torus updated under Moore neighborhoods and thresholds $SEN_{students}$ and $nonSEN_{students}$. Four scenarios (4/4, 5/4, 4/5, 5/5) and varying initial compositions are simulated, and results are validated against binomial null models using Monte Carlo sampling and Spearman correlations. The findings show that small shifts in peer influence can generate stable inclusive patterns, with Scenario 4 delivering robust empathy-driven inclusion, informing local and public policy to foster inclusive education.

Abstract

This study presents a theoretical model for identifying emergent scenarios of inclusiveness related to student with special education needs (SEN). Based on variations of the Shelling model of segregation, we explored the propagation of thinking about others as equals (empathy) in students with and without $SEN$ in school environments. We use the complex systems approach for modeling possible scenarios of inclusiveness in which patterns of empathy between students emerge instead of the well-known behavior of segregation. Based on simple transitional rules, which are evaluated by a set of null models, we show the emergence of empathy between students in school environments. Findings suggest that small variations in the incentive of students for being considered as $SEN$ generate the presence of inclusive patterns. In other situations, patterns of segregation are commonly presented.

Figures (6)

• Figure 1: An example of the basic features of CAs. (a) 2D array of 5x5 dimensions. (b) Each integer is associated with a qualitative data. (c) Local rules are applied to all cells. (d) Updating each array from $t(0)$ to $t(n)$.
• Figure 2: Types of neighborhoods. (a) von Neumann. (b) Moore. In this case we are interested in using the Moore neighborhood.
• Figure 3: Flowchart of the model validation.
• Figure 4: Escenarios of empathy by percentage of $nonSEN$ students. Escenario 1, segregation: $SEN_{students} = 4$ and $nonSEN_{students} = 4$. Escenario 2, convergence of empathy: $SEN_{students} = 5$ and $nonSEN_{students} = 4$. Escenario 3, convergence towards empathy: $SEN_{students} = 4$ and $nonSEN_{students} = 5$. Escenario 4, convergence of empathy: $SEN_{students} = 5$ and $nonSEN_{students} = 5$.
• Figure 5: Escenarios of empathy propagation. Escenario 1, segregation: $SEN_{students} = 4$ and $nonSEN_{students} = 4$. Escenario 2: $SEN_{students} = 5$ and $nonSEN_{students} = 4$. Escenario 3: $SEN_{students} = 4$ and $nonSEN_{students} = 5$. Escenario 4: $SEN_{students} = 5$ and $nonSEN_{students} = 5$.