Towards Deep Learning using Immediate Response Systems (IRS) in online university environments

Abstract

This chapter explores the potential of Immediate Response Systems (IRS) to promote deep learning in online university settings. Deep learning, as distinguished from surface learning, involves the ability to understand, transfer, and apply knowledge meaningfully, engaging students both cognitively and emotionally. Drawing from research in educational psychology, the author highlights that effective learning depends on attention, working memory, and long-term memory, and that teaching strategies should align with these principles. IRS tools—such as Wooclap, Mentimeter, or Nearpod—offer real-time interaction, feedback, and data analysis, making them well-suited for online learning environments. They encourage student participation, maintain attention, and facilitate the retrieval and application of knowledge, particularly when used strategically to activate prior knowledge, support reflection, and assess understanding. These systems can integrate multiple question types, ensure anonymity to reduce anxiety, and promote equity in participation. The chapter also aligns IRS advantages with Rosenshine’s ten principles of instruction, showing how they support review, question-asking, guided practice, feedback, and differentiated instruction. By incorporating IRS into online teaching, educators can create more interactive, adaptive, and student-centred learning environments. Ultimately, while IRS tools are not a panacea, their evidence-informed use can significantly enhance the quality of instruction and student engagement. The chapter calls for an educational culture shift grounded in research and innovation, where technology serves pedagogical goals and supports lifelong learning in a dynamic and increasingly digital educational landscape.