Behavioural Computation: Analysis, Models, and Algorithms for Supporting Human Improvement on the Web

行为计算：支持网络人类改进的分析、模型和算法

• 批准号: RGPIN-2018-06195
• 负责人: Anderson, Ashton
• 金额: $ 2.4万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713137
• 关键词: Behavioural; Computation; Analysis; Models; Algorithms

The goal of this project is to develop new knowledge, models, and algorithms to support human learning and improvement in online social systems. A wide array of online social systems support people wanting to make progress. People want to learn new skills, improve their performance in personal and professional endeavours, and make better decisions---and do so in increasingly massive numbers online. For example, Wikipedia is an ever-growing global collaborative effort to summarise the world's knowledge, which others can learn from; tools like Duolingo and Coursera are learning platforms that support enthusiastic learners; and the trend towards quantifying and sharing our progress in activities like exercising is supported by sites like Facebook and Strava. In online systems such as these, people set goals and have external targets to aim for, and are motivated to achieve these milestones. The recent availability of these massive-scale datasets of years of human behaviour makes possible a new kind of computational science of motivation, improvement, and learning. Such a computational science is increasingly necessary, as the scale of these systems raises new algorithmic and computational challenges. By observing behavioural traces of millions of people trying to make progress, learn, and improve, we will observe large-scale patterns of human improvement that were previously invisible. We will use this knowledge to model paths of progress at an unprecedented resolution, heterogeneity, and accuracy. Using these models, we will then develop design principles and algorithms that support people trying to improve in a scientifically rigorous and informed way. This research is important for the millions of Canadians who use online social systems like Wikipedia, Coursera, and Facebook to learn and improve. These systems have been designed with our current understanding of human improvement, which has not yet fully exploited the new availability of massive datasets of people making progress online. Computational challenges like predicting exactly who is likely to be motivated by what kind of goal and when will be addressed and solved. When implemented on a large scale, algorithms using this much more detailed understanding will be able to personalize goals to best motivate people on an individual level. A little bit of extra motivation, multiplied over many goals and potentially thousands or even millions of people, would result in a significant boost for productivity and achievement across many domains.

这个项目的目标是开发新的知识、模型和算法，以支持在线社交系统中的人类学习和改进。 各种各样的在线社交系统支持想要取得进步的人们。人们想要学习新的技能，在个人和职业努力中提高自己的表现，并做出更好的决定-而且越来越多的人在网上这样做。例如，维基百科是一个不断增长的全球合作努力，旨在总结世界上的知识，其他人可以学习；像Duolingo和Coursera这样的工具是支持热情学习者的学习平台；量化和分享我们在锻炼等活动中的进展的趋势得到了Facebook和Strava等网站的支持。在这样的在线系统中，人们设定目标，有外部目标要瞄准，并有动力实现这些里程碑。 最近这些关于人类多年行为的大规模数据集的出现，使一种关于动机、改进和学习的新型计算科学成为可能。随着这些系统的规模提出了新的算法和计算挑战，这样的计算科学变得越来越必要。通过观察数百万人试图取得进步、学习和改进的行为痕迹，我们将观察到以前看不见的大规模人类进步模式。我们将利用这些知识以前所未有的分辨率、异质性和准确性来模拟进步的路径。使用这些模型，我们将开发设计原则和算法，以支持人们试图以科学严谨和知情的方式进行改进。 这项研究对数百万使用维基百科、Coursera和Facebook等在线社交系统学习和提高的加拿大人来说很重要。这些系统是根据我们目前对人类进步的理解而设计的，还没有充分利用网上进步的海量数据集的新可用性。计算方面的挑战，比如准确地预测谁可能受到什么类型的目标的激励，以及何时将被处理和解决。当大规模实施时，使用这种更详细的理解的算法将能够个性化目标，从而在个人层面上最好地激励人们。一点点额外的动力，乘以许多目标和潜在的数千甚至数百万人，将导致许多领域的生产力和成就的显著提升。