Title: Exploring computer science students' learning of sensor-driven mobile app design: a case study
Authors: Joon Suk Lee; Kostadin Damevski; Hui Chen
Addresses: Department of Engineering and Computer Science, Virginia State University, Petersburg, Virginia, 23806, USA ' Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, 23284-3019, USA ' Department of Engineering and Computer Science, Virginia State University, Petersburg, Virginia, 23806, USA
Abstract: Sensor-driven applications, implemented using modern mobile or gaming devices, have great potential in motivating computer science students. Recent industry trends toward including more sensors on devices such as mobile phones, which enable new applications in health monitoring, smart homes, and human safety, among others, indicate that the number of such sensor-driven applications will continue to rise. Via a study to learn the difficulties that a group of students face in designing such sensor-driven applications, we uncover a set of instructional principles for instructors to follow in using sensor-driven applications in classrooms. Our findings include that: 1) exposing students to sensor data earlier helps improve self-efficacy; 2) focusing on extracting overall patterns from sensor data rather than understanding specifics of physical quantities is beneficial; 3) good sensor data visualisation is beneficial to design, but bad visualisation can confuse students.
Keywords: sensor-driven applications; case study; qualitative research; computer science education; student learning; mobile app design; mobile apps; self-efficacy; pattern extraction; data visualisation; sensor data.
International Journal of Teaching and Case Studies, 2016 Vol.7 No.3/4, pp.187 - 206
Received: 12 Jan 2016
Accepted: 27 Jul 2016
Published online: 12 Dec 2016 *