Building a Big Data Analytics Workforce in iSchools

该项目的意义和重要性在于将大数据分析引入教育领域。大数据行业对技术人才的需求越来越大，但大学现有的大数据课程主要侧重于具有强大计算背景的学生，忽视了很大一部分学生，他们可能会在这一重要领域接受教育和培训，但他们将在工作场所面临大数据问题。该项目旨在满足国家对拥有大数据知识的专业人员的需求，并扩大大数据分析人才库。 这项工作的一部分将涉及研究新开发的学习模块是否能更有效地提高学生的大数据能力，例如，知识，技能和分析。这个项目的目标是开发三个创新的学习模块。这些模块将被设计为：（一）利用基于小组和情境的学习方法，（二）适用于和访问的学生在学科以外，但与主流计算机科学（例如，iSchools）。第一个模块将涉及数字练习，学生将被要求发展自己关于大数据在解决现实生活中问题的相关性和重要性的叙述，并将有望成为知识渊博，精通，大数据的概念和应用。第二个模块将更具技术性，并将让学生发现大数据概念在解决信息安全实际问题方面的功效。最后，第三个模块将介绍大数据挖掘中更高级的主题，例如检查大量复杂的数据以挖掘重要的模式和知识，并介绍如何解释结果以在特定背景下做出适当的决策。围绕学习效果的研究问题的分析将采用准实验设计，使用控制组的前测和后测。学生将选择一个课程部分，而不知道哪个部分将包括新的学习模块。 在分析数据时，将使用分层线性建模方法来评估干预措施的有效性。

The significance and importance of this project resides in the introduction of big data analytics into the education landscape. There is increasing demand for skilled personnel in big data industries, but existing big data curricula at the university level focus primarily on students with a strong computational background, ignoring a large segment of students who might otherwise pursue education and training in this vital area, but who will be faced with big data issues in the workplace. This project aims at addressing the national demand for professionals with knowledge in big data and broadening the pool for a big data analytics workforce. Part of this effort will involve research as to whether the newly developed learning modules are more effective at increasing students' big data competencies, e.g., knowledge, skills, and analysis.The goal of this project is to develop three innovative learning modules. These modules will be designed to: (i) utilize both group-based and contextualized learning methods and (ii) be applicable and accessible to students majoring in disciplines outside, but related to main-stream computer science (e.g., iSchools). The first module will involve digital exercises where students will be asked to develop their own narratives about the relevance and significance of big data in solving real-life problems and will be expected to become knowledgeable of, and proficient with, big data concepts and applications. The second module will be more technical in nature and will allow students to discover the efficacy of big data concepts in solving practical problems in information security. Finally, the third module will introduce more advanced topics in big data mining, such as examining a large amount of complex data to unearth important patterns and knowledge, and introducing how to interpret the results to arrive at appropriate decisions in a specific context. Analysis of the research question surrounding the learning effectiveness will employ quasi-experimental designs that use pretests and posttests with control groups. Students will choose a course section without knowledge of which section will include the new learning modules. In analyzing the data, a hierarchical linear modeling approach will be used to evaluate the effectiveness of the intervention.