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

该项目的目标是开发新的知识、模型和算法，以支持人类在在线社会系统中的学习和改进。各种各样的在线社会系统支持着想要取得进步的人们。人们希望学习新技能，提高个人和职业表现，做出更好的决策——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项目，它总结了世界上的知识，其他人可以从中学习；Duolingo和Coursera等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取得的进展的趋势得到了Facebook和Strava等网站的支持。在诸如此类的在线系统中，人们设定目标，并有外部目标，并有动力实现这些里程碑。最近这些人类行为多年的大规模数据集的可用性使得一种新的关于动机、改进和学习的计算科学成为可能。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。通过观察数百万人努力取得进步、学习和提高的行为痕迹，我们将观察到以前看不见的人类进步的大规模模式。我们将利用这些知识以前所未有的分辨率、异质性和准确性来建立进步道路的模型。使用这些模型，我们将开发设计原则和算法，以支持人们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很重要。这些系统是根据我们目前对人类进步的理解设计的，还没有充分利用人们在网上取得进步的大量新数据集。计算挑战，比如准确预测谁可能会被什么样的目标所激励，以及何时会被解决。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，在许多目标和潜在的数千甚至数百万人的基础上，将导致在许多领域的生产力和成就的显著提高。

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.