Building a Big Data Analytics Workforce in iSchools

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.