Integrated Active Learning Framework for Biomedical BD2K

生物医学 BD2K 集成主动学习框架

• 批准号: 9132271
• 负责人: Pavel A Pevzner
• 金额: $ 21.28万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-29 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9132271
• 关键词: Active Learning; Algorithms; Award; Big Data to Knowledge; Bioinformatics; Communities; Data Analyses; Data Set; Development; Education; Enrollment; Environment; Equipment and supply inventories; Exposure to; Feedback; Fostering; Free Will; Goals; Health; Individual; Learning; Learning Module; Problem Sets; Reader; Research Personnel; Research Project Grants; Resources; Software Tools; Students; Testing; Textbooks; Thinking; Time; United States National Institutes of Health; Universities; Work; Writing; adaptive learning; base; big biomedical data; design; educational atmosphere; experience; falls; learning materials; massive open online courses; member; prevent; professor; programs; repository; self-directed learning; skills; symposium; teaching assistant; tool; web site

DESCRIPTION: The proposed project will create active and adaptive open online resources for students and educators. We propose the development of two massive open online courses (MOOCs) for Biomedical Big Data (BBD). BBD for Bioinformaticians will be aimed at bioinformatics students who know some introductory programming and need specialized tutorials focusing on BBD analysis. BBD for Biologists will provide biologists having no previous exposure to programming with the skills required to effectively apply existing software tools in BBD. These MOOCs will have three different adaptive learning tracks that will help guide readers through the courses based on their computational experience. Creating such an adaptive environment would not be possible without our substantial experience in offering the first bioinformatics MOOC, Bioinformatics Algorithms, in fall 2013 on Coursera. By making our learning materials open for use by individual learners and professors, we hope to bring down resource barriers that have prevented BBD courses from growing at individual universities. We will also develop two new problem tracks on our online Rosalind platform that facilitates independent learning of bioinformatics through automatically tested challenges. One of these problem sets will focus on implementing the algorithms required for BBD analysis; the second problem set will focus on applying existing online tools to analyze BBD. By creating a comprehensive set of assessments, we will eliminate the need for BBD professors to ever again think about automating their own homework assignments. Combined with the efforts of our open, adaptive learning environment, these problem sets will help reduce the barriers to creation of new BBD courses at universities. We will foster an open community of BBD educators by forming the BBD Education Alliance. This network will be founded at the RECOMB Conference on Bioinformatics Education at UCSD in 2015, which will focus on BBD education. Members in the alliance will create open learning modules to supplement our content as well as provide feedback to other members of the alliance on their modules. These educators will also work to design BBD courses at their own universities. Finally,

描述：拟议的项目将为学生和教育工作者创造积极而自适应的在线资源。我们建议开发两个用于生物医学大数据（BBD）的大规模开放在线课程（MOOC）。生物信息学家的BBD将针对了解一些入门编程的生物信息学学生，并且需要专门的教程，重点是BBD分析。生物学家的BBD将为生物学家提供以前未接触编程的生物学家，并在BBD中有效地应用现有的软件工具所需的技能。这些MOOC将具有三种不同的自适应学习轨道，可以根据其计算经验来指导读者学习课程。如果没有我们在2013年秋季在Coursera上提供第一家生物信息学MOOC，生物信息学算法的经验，就无法创建这种适应性环境。通过使我们的学习材料开放，以供个人学习者和教授使用，我们希望摆脱能够阻止BBD课程在个人大学中成长的资源障碍。我们还将在我们的在线Rosalind平台上开发两个新的问题轨道，这些问题通过自动测试的挑战来促进生物信息学的独立学习。这些问题集之一将集中于实施BBD分析所需的算法；第二个问题集将集中于应用现有的在线工具分析BBD。通过创建一系列全面的评估，我们将消除BBD教授再次考虑自动化自己的家庭作业的需求。结合我们开放的自适应学习环境的努力，这些问题集将有助于减少大学在大学创建新的BBD课程的障碍。我们将通过组建BBD教育联盟来培养BBD教育工作者的开放社区。该网络将于2015年在UCSD举行的重新梳理会议上建立，该会议将重点放在BBD教育上。联盟中的成员将创建开放的学习模块，以补充我们的内容，并向其模块的其他成员提供反馈。这些教育者还将努力在自己的大学设计BBD课程。最后，