在大型机构使用非正式手段有效地扩展计算培训

Dhruva K. Chakravorty, Marinus Pennings, Hong-gang Liu, Z. Wei, D. M. Rodriguez, Levi T. Jordan, Donald McMullen, Noushin Ghaffari, Shaina D. Le
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引用次数: 8

摘要

高性能计算(HPC)中心提供的短期课程为有抱负的网络基础设施(CI)专业人员提供了学习研究计算所需技能的途径。这类课程是全国各地大学和高性能计算站点的主要课程。这些短期课程提供了一种非正式的短期、强化和应用微课程模型,解决了计算方面的通用能力,而不是内容专业知识。知识的复杂程度在辅修以下的水平上教授,应用领域内容的负担落在学习者身上。自2017年春季学期以来,德州农工大学高性能研究计算(TAMU HPRC)在其短期课程项目中引入了一系列干预措施,使参与人数增长了300%。在这里,我们将介绍TAMU HPRC在短期课程模块教学中采用的策略和最佳实践。我们提出了一份纵向报告,评估自2017年春季学期以来这些策略的成功。这些数据表明,改变学生的学习方式,重新设想在院校广泛采用的分层教学模式,可能有利于学生的学习成果。
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Effectively Extending Computational Training Using Informal Means at Larger Institutions
Short courses offered by High Performance Computing (HPC) centers offer an avenue for aspiring Cyberinfrastructure (CI) professionals to learn much-needed skills in research computing. Such courses are a staple at universities and HPC sites around the country. These short courses offer an informal curricular model of short, intensive, and applied micro-courses that address generalizable competencies in computing as opposed to content expertise. The degree of knowledge sophistication is taught at the level of below a minor and the burden of application to domain content is on the learner. Since the Spring 2017 semester, Texas A&M University High Performance Research Computing (TAMU HPRC) has introduced a series of interventions in its short courses program that has led to a 300% growth in participation. Here, we present the strategies and best practices employed by TAMU HPRC in teaching short course modules. We present a longitudinal report that assesses the success of these strategies since the Spring semester of 2017. This data suggests that changes to student learning and a reimagination of the tiered instruction model widely adopted at institutions could be beneficial to student outcomes.
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