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.

我的项目将探讨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，该模型提供了一个原则上的描述，说明一个领域中的学习如何转移到另一个领域。 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实验以系统地研究人为生成的技能和概念理解领域之间学习的程度和限制（此处称为“学习领域”）。使用Icon Lab先前的工作（Brookes等，2019，行为研究方法）构建的基于VR的实验范式，我将收集有关人工学习域相似性与某些序列呈现时可以学习域的效率之间关系的数据。将使用计算生成的学习域，允许量化和控制任何一对域之间的相关性程度。该项目中开发的模型随后可以应用于人类学习者的教育课程设计，以及开发更强大的机器学习算法。