自适应学习:溯源、前景与误区

Adaptive learning: The premise, promise, and pitfalls

直至不久前,面向成千上万学习者大规模提供个性化教育纯属天方夜谭。得益于普适计算能力、大规模用户群体和可扩展分析算法,现在似乎比以往任何时候都更有可能通过自适应学习的形式,基于学习者的输入而非教师的直觉改变学习者个体的学习体验。这篇综述旨在向工程教育领域负责人和工程专业教师提供三个方面的指导。首先,解释什么是自适应学习系统以及这种系统需要什么类型的数据;其次,对自适应系统的主要用例及可能性进行分类;最后,简要阐述现阶段自适应系统的局限以及值得关注的地方。工程专业的负责人和教师可以据此判断他们的教学环境是否适合使用这些系统;教育研究者则可以研究现有系统的特点,了解它们能在哪些方面发挥重要作用。

Until recently,precisely personalizing education at scale for thousands or more learners at a time has been science fiction.With ubiquitous computational power,large user communities,and scalable analytical algorithms,it seems more possible than ever to change what a single learner experiences based on learner input rather than teacher intuition in the form of adaptive learning.In this review paper,we aim to provide guidance to engineering education leaders and engineering faculty via three main goals:first,to explain what adaptive systems are and what kinds of data they require,second,to categorize the main use cases and possibilities of adaptive systems,and third,to outline the current limitations and concerns surrounding adaptive systems.Engineering leaders and instructors can then determine if their pedagogical context is amenable to deploying these systems,and education researchers can navigate the current systems.characteristics to find areas where to make impactful contributions.