Situating Big Data: Assessing Game-Based STEM Learning in Context

这个真实的项目源于2013年关于数据密集型研究以改善教学的征集。这项工作的目的是将来自不同学科的研究人员聚集在一起，以促进新的，变革性的，多学科的方法，使用大型教育相关数据集中的数据，创造可操作的知识，以改善STEM教学和学习环境在中期和革命性的学习在长期内。该项目小组旨在了解如何使用从使用学习技术的环境中收集的数据来做以下事情：（1）允许自动评估，在提供定制反馈建议时考虑到围绕技术使用的所有课堂活动和讨论;（2）更好地了解学习和它发生的背景如何相互作用;（3）更好地了解学习和学习的环境。及（3）提供以理论为基础及以实证为基础的意见，以改善学习方法及活动。这将使教师更容易管理正在进行的评估，并使课堂活动适应以学习者为中心，以项目为基础，以探究为驱动的学习环境中学习者的需求。该项目的成果将为定期、例行和持续进行评估奠定基础，并将更全面的学习活动纳入考虑。反过来，这将允许更好的个性化和持续的反馈和学习者的脚手架。结果将提高如何评估和促进不仅学科学习的理解，但也倾向，身份发展，和长期参与。PI寻求将情境认知理论与大数据分析相结合。他们将探索如何将来自技术的点击流数据与描述技术使用环境的多模态数据的关键形式相结合，例如，个人和群体话语（在线和室内），个人和课程文物，课堂评估，和学校的表现，以产生一个数据驱动的方法：（1）了解在技术丰富的学习环境中发生的学习;（2）评估个人在这些环境中的发展和需求，建议适应和脚手架;（3）调查情境认知。他们的目标是使其更容易管理正在进行的评估，并调整课堂活动，以学习者为中心，基于项目和探究驱动的学习环境中学习者的需求。他们将研究如何（1）在评估学习和参与时考虑整个学习生态系统和收集的数据，以及（2）确定什么是有效的，什么是无效的，以促进学习。他们将展示当学习者从事动手和话语丰富的学习活动时，有用的数据在学习生态中的位置和方式，以及如何使用这些数据来评估干预措施的有效性和影响。他们的计划包括将手工编码的定性数据中的重要模式与自动收集的数据模式相匹配;这将使他们能够识别自动数据收集中的模式，这些模式可以用作理解，困惑，学习和参与等因素的指标。

This REAL project arises from the 2013 solicitation on Data-intensive Research to Improve Teaching and Learning. The intention of that effort is to bring together researchers from across disciplines to foster novel, transformative, multidisciplinary approaches to using the data in large education-related data sets to create actionable knowledge for improving STEM teaching and learning environments in the medium term and to revolutionize learning in the longer term. The project team aims to understand how to use data collected from the environment in which learning technologies are used to do the following: (1) allow automated assessment that takes the full range of classroom activities and discussions around use of the technology into account in providing customized feedback recommendations; (2) come to better understand how learning and the context in which it is happening interact; and, (3) provide theory-informed and evidence-based advice for refining learning approaches and activities. This will make it easier for teachers to manage ongoing assessment and to adapt classroom activities to learners' needs in learner-centered, project-based, and inquiry-driven learning environments. Results of this project will lay the foundations for making assessment regular, routine and ongoing and to take a fuller range of learning activities into account. This, in turn, will allow better personalization and ongoing feedback and scaffolding for learners. Results will enhance understanding of how to assess and foster not only disciplinary learning, but also disposition, identity development, and long-term participation.The PIs seek to integrate theories of situated cognition with analysis of big data. They will explore how to integrate clickstream data from technology with key forms of multimodal data describing the contexts in which the technology is being used, e.g., individual and group discourse (online and in-room), individual and curricular artifacts, classroom assessments, and school performance, to generate a data-driven methodology for: (1) understanding the learning happening in technology-rich learning environments; (2) assessing development and needs of individuals within those environments in ways that will suggest adaptations and scaffolding; and (3) investigating situated cognition. They aim to make it easier to manage ongoing assessment and to adapt classroom activities to learners' needs in learner-centered, project-based, and inquiry-driven learning environments. They will investigate how to (1) enable consideration of the full ecosystem of learning and data collected across it when assessing learning and engagement, and (2) identify what is working and not working to foster learning in a situation. They will demonstrate where and how useful data are situated in the learning ecology when learners are engaged in hands-on and discourse-rich learning activities, and how to use these data to assess effectiveness and impact of interventions. Their plan involves matching important patterns in hand-coded qualitative data to patterns of automatically collected data; this will allow them to identify the patterns in automated data collection that can be used as indicators of factors such as understanding, confusion, learning, and participation.