Student Engagement and Deep Learning in the First-Year International Relations Classroom: Simulating a UN Security Council Debate on the Syrian Crisis

Abstract

This article evaluates a 'real-time' simulation where students role-play a United Nations Security Council (UNSC) negotiation over humanitarian intervention in Syria. This simulation is undertaken in a large introductory International Relations (IR) subject. The article argues that in order to achieve deep learning outcomes across the diverse, contemporary cohort of first-year university students, active learning approaches need to be employed that engage differing learning styles and preferences. Deep learning is assessed across the conceptual and metacognitive knowledge domains with two indicators: (1) students' understanding of IR concepts by applying them beyond the parameters of the Syria case; and (2) students' critical reflection on their moral reasoning elicited by the task. We evaluate 820 students across six cohorts and 21 iterations of the simulation during 2016 and 2017 with a survey instrument and formal reflection assignment. The article finds that the simulation is highly effective at consistently engaging the majority of students' interest and motivation, while illustrating the acute and sometimes tragic tension between moral and political reasoning in International Relations. We found that disrupting student's cognitive structures regarding human rights and justice stimulated not only deeper conceptual understanding, but also emotional reactions that were the catalyst for metacognitive reflection.

Crossover of Engagement Among Academic Staff and Students During COVID-19

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