Collaborative Research: Advancing the Science of STEM Interest Development through Educational Gameplay with Machine Learning and Data-driven Interviews

合作研究：通过机器学习和数据驱动访谈的教育游戏推进 STEM 兴趣发展科学

• 批准号: 2301173
• 负责人: Jaclyn Ocumpaugh
• 金额: $ 41.95万
• 依托单位: University of Pennsylvania
• 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: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2301173&HistoricalAwards=false
• 关键词: Collaborative; Research; Advancing; Science; STEM

The integration of digital games into STEM education has been an active area of research for quite some time, but details about how students' interactions with educational games may or may not reflect their interest is more difficult to obtain. This project will use a Minecraft-based simulation environment to advance understanding of how educational digital games can support the development of enduring STEM interest. Middle school students in summer and afterschool camps will experiment with a variety of scientific topics in the What-If Hypothetical Implementations in Minecraft (WHIMC) learning system while researchers interview them at key points in their gameplay to better understand how their interest is developing. In this way, the project will contextualize how decisions made by students while engaging with the educational game are related to their prior STEM interest and how they may, in turn, influence the development of enduring STEM interest. This work will contribute advanced tools and methodological resources for studying STEM learning and interest that will help broaden participation in STEM.Hidi and Renninger's (2006) model of interest development propose four phases that correspond with students' acquisition of knowledge on a topic. In the first two phases, students may need situational triggers (such as those that are afforded in popular digital games) to sustain their interest and motivation, but to advance to the later stages of sustained, individualized interest, they must also acquire knowledge. Research on how student STEM interest develops during learning activities has typically relied on a handful of methods, each with their own limitations. Standardized survey methods, for instance, may capture important changes in students' interest level, but do not necessarily capture important details on the processes required to increase students' interest. This project will take a novel approach, using machine learning to trigger an alert to researchers when the software detects an activity (or lack thereof) likely to be tied to student interest. This will allow researchers to capture the students' experiences in situ, interviewing them before they have time to either forget or reconceptualize the event. The studies will take place in the context of WHIMC, a Minecraft-based learning environment that provides afterschool and summer educational opportunities to low-income families and to students with backgrounds traditionally underrepresented in STEM. Researchers will triangulate the interviews with more traditional measures of interest development, log data of student activities, and measures of STEM knowledge to better understand how these experiences relate to student engagement and their development of sustained, individualized interest. In doing so, researchers can explore the range of ways in which interest emerges across diverse student populations.This project is supported by NSF's 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. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent.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.

将数字游戏整合到STEM教育中已经是一个活跃的研究领域，但关于学生与教育游戏的互动如何反映他们的兴趣的细节却很难获得。该项目将使用基于《我的世界》的模拟环境来促进对教育数字游戏如何支持持久STEM兴趣发展的理解。中学生们将在夏令营和课外夏令营中，在《我的世界》（What-If Hypothetical Implementations in Minecraft，简称WHIMC）学习系统中尝试各种科学主题，而研究人员则会在他们游戏的关键时刻采访他们，以更好地了解他们的兴趣是如何发展的。通过这种方式，该项目将背景化学生在参与教育游戏时所做的决定如何与他们之前的STEM兴趣相关，以及他们如何反过来影响持久的STEM兴趣的发展。这项工作将为研究STEM学习和兴趣提供先进的工具和方法资源，有助于扩大STEM的参与。Hidi和Renninger（2006）的兴趣发展模型提出了四个阶段，与学生对一个主题的知识获取相对应。在前两个阶段，学生可能需要情境触发器（如流行数字游戏中提供的那些）来维持他们的兴趣和动机，但为了进入持续的个性化兴趣的后期阶段，他们还必须获得知识。关于学生在学习活动中如何发展STEM兴趣的研究通常依赖于几种方法，每种方法都有自己的局限性。例如，标准化的调查方法可以捕捉到学生兴趣水平的重要变化，但不一定能捕捉到提高学生兴趣所需过程的重要细节。该项目将采用一种新颖的方法，当软件检测到可能与学生兴趣相关的活动（或缺乏活动）时，使用机器学习向研究人员发出警报。这将使研究人员能够就地捕捉学生的经历，在他们有时间忘记或重新定义事件之前对他们进行采访。这些研究将在WHIMC的背景下进行，这是一个基于《我的世界》的学习环境，为低收入家庭和传统上在STEM中代表性不足的背景的学生提供课后和暑期教育机会。研究人员将用更传统的兴趣发展指标、学生活动日志数据和STEM知识指标对访谈进行三角测量，以更好地了解这些经历与学生参与以及他们持续、个性化兴趣的发展之间的关系。通过这样做，研究人员可以探索不同学生群体产生兴趣的各种方式。本项目由美国国家科学基金会EDU核心研究（ECR）项目支持。ECR项目强调在该领域产生基础知识的基础STEM教育研究。投资在至关重要、广泛和持久的关键领域：STEM学习和STEM学习环境，扩大STEM参与，以及STEM劳动力发展。该项目支持积累有力的证据，为理解、建立理论解释、提出干预和创新建议提供依据。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。