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Designing for Meaningful Learning

K. Ann Renninger, Sanna Järvelä

发表年份
2022
引用次数
5

摘要

Understanding individuals’ interest, motivation, and engagement is essential to designing for meaningful learning. We typically think of engaged learners as those who have a more developed interest in content (e.g., math, robotics, swimming) and are motivated to learn. But learners who are not engaged or who are unmotivated can also be assisted to meaningfully engage with content in ways that lead to deep learning. This chapter summarizes research on two questions for how to design for meaningful learning: What supports unmotivated individuals to become motivated to learn? How do we design tasks that enable those who are already engaged to continue to deepen their interest? The chapter summarizes five research studies that provide converging evidence that designing for meaningful learning requires (1) addressing the differences in learners’ interest, motivation, and engagement; (2) supporting learners in engaging in thinking about content with others. Learning environments can be designed to enable all learners, regardless of their initial engagement with material, to develop meaningful connections to content, thus optimizing their learning.

关键词

Learning designActive learning (machine learning)Content (measure theory)PsychologyMathematics educationComputer scienceArtificial intelligence

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