Technologies to Support Online Knowledge Sharing and Retrieval for Feature-Rich Software Applications

支持功能丰富的软件应用程序在线知识共享和检索的技术

• 批准号: RGPIN-2020-05317
• 负责人: Bunt, Andrea
• 金额: $ 2.99万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749784
• 关键词: Technologies; Support; Online; Knowledge; Sharing

Feature-rich software applications (e.g., Photoshop, Fusion 360, AutoCAD, Excel) enable a wide range of tasks through extended command and tool sets. Their widespread functionality and domain-specific vocabulary, however, can make it difficult for users to get started and to expand their skills. A relatively new reality of software learning is the loosely connected and distributed nature of help resources, with the Internet now ripe with materials (e.g., tutorials, discussion forums, Q&A sites) authored by other users. There are currently unparalleled opportunities for user communities to contribute resources that address the unique abilities, contexts and desired outcomes of an application's diverse user base. There is also the potential for other users to leverage these tailored resources to grow their skills. At the same time, current tools and platforms are not necessarily eliciting the perspectives of all user groups, and finding appropriate help resources can be a significant source of user frustration. My long-term objective is to inform and develop technologies that make contributing to and navigating this complex help ecosystem a seamless and rewarding activity for all feature-rich software users. For the research in this funding cycle, I focus on three short-term objectives: 1) Creating technologies that widen the pool of software users contributing their knowledge and experiences online, emphasizing two underrepresented groups (women and children). 2) Creating technologies for exploratory learners, who want to find materials that expand their software skills. 3) Investigating new evaluation approaches that increase and broaden participant pools, and capture new measures of software learning. Following established best practices in Human-Computer Interaction (HCI) research, my HQP and I will conduct mixed-method formative studies to create novel technologies that support users in contributing to and finding appropriate online external resources for feature-rich software. In developing our underlying algorithms, we will investigate machine learning, natural language processing, and collaborative filtering. We will validate our approaches and techniques through empirical evaluations with the intended target populations both in the lab and in the field. The proposed research will involve training approximately 14 HQP, providing them with skills in user-centred interaction design, Web technologies, applied artificial intelligence, and quantitative/qualitative analysis techniques. Our research will produce novel prototypes and design guidelines to inform the next generation of feature-rich software and knowledge-sharing communities. The proposed research will also address important dimensions of computing diversity by illustrating ways to consider women and child audiences upfront in the design of new technologies.

丰富的软件应用程序（例如，Photoshop、Fusion 360、AutoCAD、Excel）通过扩展的命令和工具集支持各种任务。然而，它们广泛的功能和特定领域的词汇表可能会使用户难以入门和扩展他们的技能。软件学习的一个相对较新的现实是帮助资源的松散连接和分布式特性，随着互联网上的材料（例如，教程、论坛、问答站点）。目前，用户社区有着无与伦比的机会来贡献资源，以解决应用程序的不同用户群的独特能力、背景和期望结果。其他用户也有可能利用这些量身定制的资源来提高他们的技能。与此同时，当前的工具和平台并不一定能吸引所有用户群体的观点，寻找适当的帮助资源可能是用户沮丧的重要来源。 我的长期目标是为所有功能丰富的软件用户提供信息和开发技术，使对这个复杂的帮助生态系统的贡献和导航成为一种无缝和有益的活动。对于本资助周期的研究，我专注于三个短期目标：1）创造技术，扩大软件用户的库，在线贡献他们的知识和经验，强调两个代表性不足的群体（妇女和儿童）。2)为探索性学习者创建技术，他们希望找到扩展其软件技能的材料。3)调查新的评估方法，增加和扩大参与者池，并捕捉软件学习的新措施。根据人机交互（HCI）研究中的最佳实践，我和HQP将进行混合方法的形成性研究，以创建新技术，支持用户为功能丰富的软件做出贡献并找到合适的在线外部资源。在开发底层算法时，我们将研究机器学习、自然语言处理和协同过滤。我们将通过实验室和现场的预期目标人群的经验评估来验证我们的方法和技术。 拟议的研究将涉及培训约14名HQP，为他们提供以用户为中心的交互设计，网络技术，应用人工智能和定量/定性分析技术的技能。我们的研究将产生新颖的原型和设计指南，为下一代功能丰富的软件和知识共享社区提供信息。拟议的研究还将解决计算多样性的重要方面，说明如何在设计新技术时首先考虑妇女和儿童受众。