CHS: Small: Non-Programmer Authoring of Data-Driven Prediction Simulations

CHS：小型：数据驱动的预测模拟的非程序员创作

• 批准号: 1816923
• 负责人: Frank Shipman
• 金额: $ 49.98万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816923&HistoricalAwards=false
• 关键词: CHS; Small; Non; Programmer; Authoring

This project explores ways to support educators as they introduce interactive data-driven activities to help learners develop intuitions about data and acquire specific analytic skills. While educators agree that the availability of data offers new educational opportunities in many disciplines, there is little consensus about how data-driven educational activities should be created. Often current solutions mandate teaching both instructors and students to program; these solutions are ambitious in scope, but can easily fall short of their aspirations, discouraging those who have difficulties learning to program and distracting the class from its primary educational goals. The prediction simulations framework enables an alternative type of activity, but how teachers perceive the opportunities and challenges offered by this type of activity, and how prediction simulations relate to their students, subject area, and educational context all pose new questions. Data analysis skills are a fundamental element of many disciplines, motivated by the widespread availability of datasets, ubiquitous sensing capabilities, and ready access to processing power. The ability to create prediction simulations has a potentially transformative effect on data science pedagogy. Concepts can be introduced to learners at different levels and disciplines to motivate learners and keep them engaged. Besides allowing learners to "dig into" data to develop their own understanding and construct better mental models of the underlying phenomena, prediction simulations can deepen a learner's understanding and intuition for the use of data in other domains. This project will provide an understanding of how teachers view the increasing availability of data in their domains, the tools and curricular activities currently available to them, and the overhead of creating and tailoring activities to their environment. Towards that goal, two interacting lines of research will be pursued: a multi-method study of the non-programmers who might create prediction simulations for use in pedagogical settings; and the design and evaluation of techniques to support authoring of prediction simulations. These two intertwined activities will help answer the following research questions: (1) What are the characteristics of domains, educators and students, and pedagogical situations that center on data analysis skills? Which would benefit from and be amenable to the introduction of prediction simulations? (2) Can generalized capabilities be developed to support the creation of novel situation-specific prediction simulations based on new combinations of datasets, visualizations, and analytic tools? (3) What are the most effective ways to support the authoring of new prediction simulations by non-programmers? Methods to be investigated include template-based and specification-based authoring.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目探索了支持教育工作者引入交互式数据驱动活动的方法，以帮助学习者培养对数据的直觉并获得特定的分析技能。虽然教育工作者同意数据的可用性为许多学科提供了新的教育机会，但对于如何创建数据驱动的教育活动，几乎没有达成共识。通常，当前的解决方案要求教师和学生同时学习编程；这些解决方案在范围上是雄心勃勃的，但很容易达不到他们的愿望，使那些在学习编程方面有困难的人感到沮丧，并使班级偏离其主要的教育目标。预测模拟框架支持另一种类型的活动，但是教师如何看待这种类型的活动所提供的机会和挑战，以及预测模拟如何与他们的学生、学科领域和教育环境相关联，这些都提出了新的问题。数据分析技能是许多学科的基本要素，其动机是数据集的广泛可用性、无处不在的传感能力和随时可用的处理能力。创建预测模拟的能力对数据科学教学具有潜在的变革性影响。可以向不同层次和学科的学习者介绍概念，以激励学习者并保持他们的参与。除了允许学习者“挖掘”数据以发展自己的理解并构建更好的潜在现象的心理模型外，预测模拟还可以加深学习者对其他领域数据使用的理解和直觉。该项目将帮助了解教师如何看待他们领域中日益增加的数据可用性、他们目前可用的工具和课程活动，以及根据他们的环境创建和定制活动的开销。为了实现这一目标，将进行两个相互作用的研究方向：对可能创建用于教学环境的预测模拟的非程序员进行多方法研究；设计和评估支持预测模拟创作的技术。这两个相互交织的活动将有助于回答以下研究问题：(1)以数据分析技能为中心的领域、教育者和学生以及教学情境的特征是什么？哪一个会从预测模拟的引入中受益？(2)基于数据集、可视化和分析工具的新组合，能否开发出通用的能力来支持创建新的特定情境预测模拟？(3)支持非程序员编写新的预测模拟的最有效方法是什么？要研究的方法包括基于模板和基于规范的创作。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Who Broke Amazon Mechanical Turk?: An Analysis of Crowdsourcing Data Quality over Time

谁破坏了 Amazon Mechanical Turk？：随时间变化的众包数据质量分析

• DOI: 10.1145/3578503.3583622
• 发表时间: 2023
• 期刊: WebSci '23: Proceedings of the 15th ACM Web Science Conference 2023
• 作者: Marshall, Catherine C.;Goguladinne, Partha S.R.;Maheshwari, Mudit;Sathe, Apoorva;Shipman, Frank M.
• 通讯作者: Shipman, Frank M.

Keeping People Playing: The Effects of Domain News Presentation on Player Engagement in Educational Prediction Games

让人们继续玩：领域新闻呈现对玩家参与教育预测游戏的影响

• DOI: 10.1145/3372923.3404813
• 发表时间: 2020
• 期刊: HT '20: Proceedings of the 31st ACM Conference on Hypertext and Social Media
• 作者: Dzodom, Gabriel;Kulkarni, Akshay;Marshall, Catherine C.;Shipman, Frank M.
• 通讯作者: Shipman, Frank M.

Ownership, Privacy, and Control in the Wake of Cambridge Analytica: The Relationship between Attitudes and Awareness

剑桥分析之后的所有权、隐私和控制：态度和意识之间的关系

• DOI: 10.1145/3313831.3376662
• 发表时间: 2020
• 期刊: CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
• 作者: Shipman, Frank M.;Marshall, Catherine C.
• 通讯作者: Marshall, Catherine C.