Inclusive education and UDL: adapting mathematics tasks for students with special educational needs in inclusive classrooms on Ecuador’s coast
DOI:
https://doi.org/10.64747/fwek3w94Keywords:
universal design for learning UDL, inclusive education, special educational needs SEN, mathematics tasks, basic general education, EcuadorAbstract
This study assessed the effects of a bank of mathematics tasks designed according to Universal Design for Learning (UDL) principles on participation and achievement among students with special educational needs (SEN) in Basic General Education (EGB) in Guayas Province, Ecuador. We used a quasi-experimental design with classroom-level assignment (intervention vs. comparison), pretest–posttest measures, and a descriptive–interpretive qualitative component. Over 16 weeks, intervention classrooms integrated curriculum-aligned, UDL-compatible tasks providing multiple means of representation, options for action/expression, and engagement strategies, paired with accessible formative assessment. Twenty-four classrooms participated (n = 372; SEN = 104). Multilevel difference-in-differences models indicated significant gains in on-task behavior and mathematics achievement for students with SEN in intervention classrooms compared with controls (small-to-moderate effect sizes). Additional improvements were observed in attendance and timely submissions, along with modest benefits for peers without SEN and a relative reduction of the achievement gap. Implementation fidelity positively moderated effects. Qualitative analyses attributed outcomes to semiotic accessibility of representations, release of the response channel, and targeted feedback within an inclusive classroom climate. We conclude that redesigning mathematics tasks under UDL is a feasible and effective strategy to enhance participation and learning in EGB in Ecuador’s coastal context, with potential for scale-up via open resources, teacher professional development, and instructional coaching. Cluster randomized trials, longitudinal follow-ups, and component-optimization studies are recommended to strengthen causal inferences and guide wider implementation.
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