University of Malaga (SPAIN)
About this paper:
Appears in: INTED2017 Proceedings
Publication year: 2017
Pages: 3803-3811
ISBN: 978-84-617-8491-2
ISSN: 2340-1079
doi: 10.21125/inted.2017.0929
Conference name: 11th International Technology, Education and Development Conference
Dates: 6-8 March, 2017
Location: Valencia, Spain
In this work we present the experiences of using two different learning strategies in two undergraduate courses of robotics. Concretely, Matlab [1] and the Robot Operating System (ROS) [2], were considered in the conducted practical lessons for supporting the theory behind a number of typical robotic problems, namely, perception, localization, and mapping. While Matlab is a more general framework for the design an evaluation of algorithms, ROS is the de-facto software in many state-of-the-art platforms, so the skills and competences acquired by each one diverge.

We also resorted two different pedagogical techniques for presenting the practical exercises:
(i) to provide skeletons of the exercises’ code to be filled by the students and
(ii) to propose a challenge where they have to develop an algorithm for achieving a certain robot behaviour, competing with the algorithms implemented by other students.

We aimed to get feedback about their grade of motivation for facing the practical exercises from both learning strategies, since we consider it is a good indicator of the learning success.

The proposed experience was carried out in courses from two different degrees: Computer Science and Industrial Engineering, having students with different capabilities and motivations. The obtained students’ opinions were instructive, reporting, for example, that although they consider harder to master ROS when compared to Matlab, it might be more useful in their (robotic related) professional careers, which enhanced their disposition to study it. They also considered that the challenge-exercise, in addition to motivate them, helped to develop their skills as engineers to a greater degree than the skeleton-code based one. These and other conclusions will be useful in posterior courses to boost the interest and motivation of the students.

[1] Matlab.
[2] Quigley, Morgan., Conley, Ken., Gerkey, Brian P.., Faust, Josh., Foote, Tully., Leibs, Jeremy., Wheeler, Rob., and Ng, Andrew Y. ROS: an open-source Robot Operating System. ICRA Workshop on Open Source Software, 2009.

(1) The text of the exercises as well as related material will be available to the community with the full version of this paper.
Motivational Learning, Robotic Courses, Matlab, ROS.