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源中学习和概括。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解释，说明一个领域的学习如何转移到另一个领域。特别是，我将专注于调查概念和感觉运动能力之间的转移，建立在利兹大学沉浸式认知（ICON）实验室进行的现有研究的基础上（例如，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着的关系，以标准化的国家学业成绩为衡量标准; Giles等人，在这个项目的过程中，我将进行一系列实验，系统地研究人工生成的技能和概念理解领域（这里称为“学习领域”）之间的学习转移的程度和限制。使用基于ICON实验室先前工作的基于VR的实验范例（Brookes等人，2019年，行为研究方法），我将收集关于人工学习领域相似性与以特定序列呈现时可以学习的领域的效率之间关系的数据。将使用计算生成的学习领域，允许任何一对领域之间的相关程度被量化和控制。在这个项目中开发的模型可能随后被应用于人类学习者的教育课程的设计，以及更强大的机器学习算法的开发。