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

很长一段时间以来，将数字游戏融入STEM教育一直是一个活跃的研究领域，但更难获得学生与教育游戏的互动如何反映他们的兴趣的细节。这个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STEM持久兴趣的发展的理解。参加夏令营和课后夏令营的中学生将在假设我的世界(WHIMC)学习系统中试验各种科学主题，同时研究人员将在游戏中的关键点采访他们，以更好地了解他们的兴趣是如何发展的。通过这种方式，该项目将了解学生在参与教育游戏时所做的决定如何与他们先前的STEM兴趣相关，以及它们可能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的参与度。Hidi和Renninger(2006)的兴趣发展模型提出了与学生获得某一主题知识相对应的四个阶段。在前两个阶段，学生可能需要情景触发(如流行的数字游戏中提供的那些)来维持他们的兴趣和动机，但要想进入持续的、个性化的兴趣的后期阶段，他们还必须获得知识。关于学生在学习活动中如何培养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。这个项目将采用一种新的方法，当软件检测到可能与学生兴趣有关的活动(或缺乏活动)时，使用机器学习向研究人员触发警报。这将使研究人员能够现场捕捉学生的经历，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STEM传统上代表人数较少的学生提供课后和暑期教育机会。研究人员将使用更传统的兴趣发展指标对访谈进行三角测量，记录学生活动的数据，以及STEM知识指标，以更好地了解这些体验与学生参与及其持续、个性化兴趣发展的关系。通过这样做，研究人员可以探索不同学生群体产生兴趣的方式范围。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。该计划支持积累强有力的证据，为理解、构建理论以解释并建议干预和创新来解决持续存在的问题提供信息。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。

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.