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: 2301172
• 负责人: Luc Paquette
• 金额: $ 69.8万
• 依托单位: University of Illinois at Urbana-Champaign
• 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=2301172&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.

一段时间以来，将数字游戏集成到STEM教育中一直是一个积极的研究领域，但是有关学生与教育游戏的互动方式可能或可能不会反映出他们的兴趣的详细信息更难以获得。该项目将使用基于我的Minecraft的模拟环境来促进对教育数字游戏如何支持持久STEM兴趣的发展的理解。夏季和课后营地的中学生将在Minecraft（WHIMC）学习系统中实现各种科学主题，同时研究人员在游戏玩法的关键点进行了采访，以更好地了解他们的兴趣的发展。通过这种方式，该项目将使学生在与教育游戏互动时做出的决策与他们先前的STEM兴趣以及它们如何影响持久的STEM兴趣的发展有关。这项工作将为研究STEM学习和兴趣的高级工具和方法论资源提供帮助，这将有助于扩大对STEM的参与。Hidi和Renninger（2006）的兴趣开发模型提出了四个阶段，这些阶段与学生对某个主题的知识的获取相对应。在前两个阶段中，学生可能需要局势触发器（例如在流行数字游戏中提供的触发器）来维持他们的兴趣和动力，但要晋升到持续，个性化兴趣的后期，他们还必须获得知识。研究学生在学习活动中如何发展的研究通常依赖于少数方法，每种方法都有自己的局限性。例如，标准化的调查方法可能会捕获学生兴趣水平的重要变化，但不一定会捕获有关增加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，使用机器学习在软件检测到可能与学生兴趣相关的活动（或缺乏活动）时向研究人员触发警报。这将使研究人员能够捕捉到学生的原位经历，在他们有时间忘记或重新概念化活动之前对他们进行采访。这些研究将在Whimc的背景下进行，Whimc是一个基于我的Minecraft的学习环境，为低收入家庭和具有STEM中代表性不足的背景不足的学生提供后教育和夏季的教育机会。研究人员将通过更传统的兴趣开发，学生活动的日志数据以及STEM知识的衡量标准进行三角剖分，以更好地了解这些经验与学生参与及其持续，个性化的兴趣的发展如何相关。通过这样做，研究人员可以探索各种学生人群中兴趣出现的范围。该项目得到了NSF的EDU核心研究（ECR）计划的支持。 ECR计划强调了基本的STEM教育研究，该研究在该领域产生了基础知识。投资是在重要，广泛和持久的关键领域进行的：STEM学习和STEM学习环境，扩大参与STEM以及STEM劳动力的发展。该计划支持积累强大的证据，以告知努力，以理解，建立理论来解释并提出干预和创新以解决持久性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的评估来支持的。影响审查标准。