Developing Theoretically and Computationally Informed Learning Curricula

开发理论和计算知识学习课程

• 批准号: 2439173
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2439173
• 关键词: Developing; Theoretically; Computationally; Informed; Learning

My project will explore how well humans and algorithms learn and generalise from different sources of information depending on how that information is presented. My principal goal is to develop and test theoretically informed computational models that provide a principled account of how learning in one domain can transfer to another. In particular, I will focus on investigating transfer between conceptual and sensorimotor abilities, building on existing research conducted by the Immersive Cognition (ICON) Lab at the University of Leeds (e.g. evidence from my supervision team demonstrating a striking relationship between sensorimotor abilities and mathematical ability, as measured on standardised national academic attainment scores; Giles et al., 2018, Psych Science).Over the course of this project I will conduct a series of experiments to systematically investigate the extent and limits of transfer of learning between artificially generated domains of skills and conceptual understanding (referred to here as "learning domains"). Using a VR-based experimental paradigm built upon previous work by the ICON lab (Brookes et al., 2019, Behaviour Research Methods), I will gather data on the relationship between artificial learning domain similarity and the efficiency with which the domains can be learned when presented in certain sequences. Computationally generated learning domains will be used, allowing the degree of relatedness between any pair of domains to be quantified and controlled.The models developed in this project may subsequently be applied to the design of educational curricula for human learners, and the development of more robust machine learning algorithms.

我的项目将探索人类和算法如何根据信息的呈现方式从不同的信息来源学习和概括。我的主要目标是开发和测试理论上知情的计算模型，这些模型提供了一个原则性的描述，说明一个领域的学习如何转移到另一个领域。特别是，我将重点研究概念和感觉运动能力之间的迁移，以利兹大学沉浸式认知(ICON)实验室现有的研究为基础(例如，来自我的监督团队的证据表明感觉运动能力和数学能力之间存在显著的关系，以标准化的国家学术成绩衡量；Giles等人，2018年，《心理学》)。在这个项目的过程中，我将进行一系列实验，系统地调查人工生成的技能领域和概念理解之间的学习迁移的程度和限制。使用基于虚拟现实的实验范式，建立在ICON实验室(Brookes等人，2019年，行为研究方法)之前的工作基础上，我将收集关于人工学习领域相似性与以特定序列呈现时学习领域的效率之间的关系的数据。将使用计算生成的学习领域，允许量化和控制任何一对领域之间的关联程度。该项目中开发的模型随后可能被应用于为人类学习者设计教育课程，以及开发更健壮的机器学习算法。