Developing Big Data Projects in Open University Engineering Courses: Lessons Learned

Abstract

Big Data courses in which students are asked to carry out Big Data projects are becoming more frequent as a part of University Engineering curriculum. In these courses, instructors and students must face a series of special characteristics, difficulties and challenges that it is important to know about beforehand, so the lecturer can better plan the subject and manage the teaching methods in order to prevent students' academic dropout and low performance. The goal of this research is to approach this problem by sharing the lessons learned in the process of teaching e-learning courses where students are required to develop a Big Data project as a part of a final degree/master course. In order to do so, a survey was carried out among a group of students enrolled in those kinds of courses during the last years. The quantitative and qualitative analysis of the obtained data led us to present a series of lessons learned that may help other participants (both students and lecturers) to better study, design and teach similar courses. In addition, the results shed light on possible existing open problems in the area of Big Data project development. Both the methodology used and the survey designed in this research were validated by a group of experts in the area using a formal statistical approach at a significance level of p<; 0.008, which support the validity of the lessons learned.

Keywords

• Academic projects
• big data
• data mining
• data science
• NoSQL
• lessons learned