Abstract:

In most courses of computer science grades, students develop systems that are usually tested only in reduced data sets. These systems are still normally far from the common kinds of systems that current society needs. In particular, many current systems deal with huge amounts of data. Several existing works recommend teachers to promote the competence of collaborative development of solutions that can be applied in realistic scenarios. Acquiring the skill of collaborative development of scalable solutions can be useful to prepare students for the real problems that they will address in their professional careers.

The present work belongs to the current trend of works that refer to some concepts of the “big data” field as the key to teach the aforementioned skill. More concretely, the current work proposes a guideline for designing collaborative learning activities to develop solutions that can receive input from big data. These activities normally encourage students to present novel solutions for solving certain problems in realistic scenarios. Students are gathered in teams, and each of these works collaboratively to reach to the corresponding solution. In most of these activities, the teacher suggests the main topic. However, students decide which particular subtopic they will address as long as the teacher agrees with them.

The presented guideline has been experienced with a pilot study in the course of Decision Support Systems (DSS) in the computer science grade of the University of Zaragoza. In this course, the teacher showed some of the main free available big data repositories to the students. Each group of students selected a problem that could be solved by a DSS after they reached a consensus with the teacher. Students worked collaboratively to develop scalable software applications that were robust and efficient enough to use big data for supporting users’ decisions. The teacher supervised the developments of all the teams assisting them in overcoming technological problems and taking some design decisions. At the end of the course, each team of students performed a presentation of their DSS to the other students and the teacher. In addition, all the students replied a short questionnaire for measuring (a) their perception about the learning activity, and (b) the learned knowledge about DSSs. Furthermore, the classmates evaluated each DSS with two well-known user experience scales: Usefulness, Satisfaction and Ease of Use (USE) questionnaire, and the System Usability Scale (SUS).

This paper presents one of the DSSs developed by the students as an example of the final work product that students can develop in learning activities designed with the present guideline. The paper also analyzes the results of the questionnaires.

Finally, this paper discusses the possible future impact of the presented guideline and determines the next steps to extend the current approach to other courses and grades. These future steps aim at obtaining an interdisciplinary guideline for designing collaborative learning activities in which students learn to propose scalable solutions in realistic scenarios by means of big data from free available repositories.

Keywords:

Big data, collaborative learning activity, decision support system, software development education, teamwork.