EXP: Implementing a Workflow Visualization System for Design-Based Research

该项目正在迭代设计，测试，实施和评估一个完全自动化的，可共享的工作流可视化系统（WVS），以帮助研究人员和教育工作者保存，组织和分析从复杂的网络学习研究和开发（R D）工作的多个设计迭代中捕获的数据。该团队正在增强一个广泛使用的学习管理系统（Moodle），该系统具有现有的数据收集算法，新的工具可以产生研发工作两个互补方面的视觉，多层表示&：（1）复杂教育干预措施的设计，包括有关任务，使用的资源，参与者结构等变量的信息，以及可持续发展;以及（2）这些干预措施的制定实施情况，包括学习者完成任务或获取资源的数据以及学习者产生的数据。WVS最终被设想为支持反思性教学实践，分享适应性成功的干预措施和基于设计的研究（DBR）。该项目小组还在处理涉及从在线来源挖掘数据的研究中有关隐私和保护人类受试者权利的重大问题。这个项目的智力价值在于它提供的承诺，使教育研究界能够更好地记录复杂的网络学习干预措施的实施历史，包括其背景，理由，迭代和结果。目前的工作，包括这个新项目的基础工作，都依赖于手工制作这些丰富数据的表示。但这个新项目提供的是一种自动化数据可视化策略的方法，通过从Moodle网站挖掘设计信息和学生成绩数据，使其广泛可用且易于使用。该项目通过选择Moodle发挥更广泛的影响，Moodle是领先的开源学习管理系统之一，拥有超过5万个站点，支持全球约4000万用户。WVS的开源代码也允许其与其他学习管理系统一起使用。最重要的是，该项目有可能通过改进知识共享和加速采用成果和最佳做法，改变更广泛的学习科学和网络学习科学界作为单独项目和项目网络参与工作的方式。

This project is iteratively designing, beta-testing, implementing, and evaluating a fully automated, sharable Workflow Visualization System (WVS) to help researchers and educators preserve, organize, and analyze data captured from multiple design iterations of complex cyberlearning research and development (R&D) efforts. The team is augmenting a widely used learning management system (Moodle), which has existing data collection algorithms, with new tools that produce visual, multi-layered representations of two complementary aspects of R&D efforts: (1) the design of complex educational interventions, including information about variables such as tasks, resources used, participant structures, and workspaces; and (2) the enacted implementations of these interventions, including data about learners' completion of tasks or acquisition of resources and learner-produced data. The WVS is envisioned ultimately to support reflective teaching practice, sharing of adaptable successful interventions, and design-based research (DBR). The project team is also tackling significant problems regarding privacy and protecting the rights of human subjects in research that involves data mining from online sources. The intellectual merit of this project rests in the promise it offers to enable the educational research community to better document the implementation history of complex cyberlearning interventions, including their contexts, rationale, iterations and outcomes. Current efforts, including the foundational work on which this new project builds, rely on manual production of representations of such rich data. But what this new project offers is a means to automate this data visualization strategy by mining both design information and student performance data from Moodle sites, making it widely available and easy to use. The project is exercising its broader impact through its choice of Moodle, one of the leading open-source learning management systems, that has over fifty-thousand sites supporting approximately forty million users world-wide. The WVS' open-source code also permits its use with other learning management systems. Most importantly, the project has the potential to transform how the broader learning sciences and indeed cyberlearning sciences communities engage in their work, both as individual projects and as a network of projects, through improved knowledge sharing and accelerated adoption of results and best practices.