CAREER: Improving educators' trust in and effective uses of predictive learning analytics to support students

职业：提高教育工作者对预测学习分析的信任并有效利用它来支持学生

• 批准号: 2237593
• 负责人: Rene Kizilcec
• 金额: $ 80.76万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237593&HistoricalAwards=false
• 关键词: CAREER; Improving; educators; trust; effective

Predictive learning analytics (PLA) that have a focus on identifying students at risk of failing are gaining traction with leaders at all levels of education. The effective adoption and use of PLA in the US education system can promote educational attainment, STEM workforce development, and national development by supporting lower-resourced educational institutions and members of underserved communities. Prior research has focused on enhancing the technical implementation of these AI-based systems, but studies of why deployments of existing technologies have failed identify issues that are not technical but social, psychological, or cultural in nature. For example, educators’ trust in and effective use of PLA are instrumental for realizing their potential benefits, motivating this work to study how changes in the framing, level of transparency, and training can improve educators’ use of PLA. The project will involve 3,600 US educators across several large-scale studies and advance an understanding of what influences educators’ trust and effective use of PLA, as well as AI-based education technology more broadly. These insights will guide the development of an evidence-based curriculum about PLA for educators to foster awareness, trust, and effective use. The PLA training materials will be made available online and via posts on social media channels. This project develops concrete and scalable interventions to help design technologies that affect the academic progress of learners of all ages.Building on the Technology Acceptance Model and Academic Resistance Models, the project will investigate how educators respond to procedural justice framings and algorithmic transparency for AI-based education technology, especially during expectancy violation and high levels of uncertainty. Three studies will be conducted as online randomized controlled trials, replicated for robustness, that present educators with a simulated PLA to assess their perceptions, attitudes, and behavioral intentions. How different presentations of PLA influence educators’ usage intentions will be investigated by using an online noise audit, which quantifies how much the same information about students leads educators to the same conclusions about how to intervene. The cumulative insights of the project will be applied in a field experiment on a university campus to examine if evidence-based improvements to an existing at-risk identification process can encourage faculty to reach out to students and better support them. The project will contribute to significant advances in our causal understanding of ways to foster all educators’ trust and effective use of a learning analytics system. The award is funded in part by the EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This project is also funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.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.

专注于识别有失败风险的学生的预测性学习分析(PLA)正受到各级教育领导的青睐。在美国教育系统中有效采用和使用解放军可以通过支持资源较少的教育机构和服务不足的社区成员来促进教育成就、STEM劳动力发展和国家发展。以前的研究侧重于加强这些基于人工智能的系统的技术实施，但关于现有技术部署为何失败的研究发现，这些问题不是技术问题，而是社会、心理或文化性质的问题。例如，教育工作者对解放军的信任和有效使用有助于实现他们的潜在利益，激励这项工作研究框架、透明度水平和培训方面的变化如何提高教育工作者对解放军的使用。该项目将涉及3600名美国教育工作者，进行几项大规模研究，并加深对教育工作者对解放军以及更广泛的基于人工智能的教育技术的信任和有效使用的理解。这些见解将指导教育工作者开发关于解放军的循证课程，以培养意识、信任和有效使用。解放军的训练材料将在网上和社交媒体渠道上发布。该项目开发具体且可扩展的干预措施，以帮助设计影响所有年龄段学习者学习进步的技术。该项目将以技术接受模型和学术阻力模型为基础，调查教育工作者如何应对基于人工智能的教育技术的程序正义框架和算法透明度，特别是在违反预期和高度不确定性的情况下。三项研究将作为在线随机对照试验进行，重复稳健性，向教育工作者呈现模拟的解放军，以评估他们的感知、态度和行为意图。通过使用在线噪音审计，将调查不同的解放军陈述如何影响教育者的使用意图，该审计会量化关于学生的相同信息在多大程度上导致教育者在如何干预方面得出相同的结论。该项目的累积洞察力将应用于一所大学校园的实地实验，以检验对现有风险识别过程的基于证据的改进是否可以鼓励教师接触学生，并更好地支持他们。该项目将有助于在我们对培养所有教育工作者信任和有效使用学习分析系统的方法的因果理解方面取得重大进展。该奖项的部分资金来自EDU核心研究(ECR)计划。ECR计划强调基础STEM教育研究，以产生该领域的基础知识。在基本、广泛和持久的关键领域进行投资：STEM学习和STEM学习环境，扩大STEM的参与，以及STEM劳动力发展。该项目还由学生和教师创新技术体验计划(ITEST)资助，该计划支持建立对实践、计划要素、背景和过程的理解的项目，这些项目有助于提高学生对科学、技术、工程和数学(STEM)以及信息和通信技术(ICT)职业的知识和兴趣。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。