Deepening Learning through Learning-by-Inventing

Abstract

Executive Summary It has been shown that deep approaches to learning, intrinsic motivation, and self-regulated learn-ing have strong positive effects on learning. How those pedagogical theories can be integrated in computing curricula is, however, still lacking empirically grounded analyses. In a more general level, it has been widely acknowledged that in tertiary-level computing education there is a des-perate need for more creativity and innovation-friendly instructional approaches. While the use of, for example, problem-based instructional approaches into computing education is increasing, their penetration into computing pedagogy is still shallow. In many contexts of higher computing education, teaching still follows a fixed set of instructional lectures, a fixed set of short-term learning objectives, and a fixed set of predetermined take-home exercises, followed by a pen-and-paper exam. A typical way to teach software development in higher computing edu-cation is to utilize industry-standard project management principles in a project, which aims for efficient and risk-free production of software which meets the demands of a customer or a client. As an alternative to the typical conventions, this study investigated students' learning in a learn-ing environment that explicitly focused on inventing and creativity. In other words, this study integrated, in a robotics-based programming class, a method of learning-by-inventing and studied its qualitative effects on students' learning through 144 interviews. Five findings were related with learning theories: changes in students' problem management cy-cle, problem-rich learning environment, conceptions of the nature of computing, extension of deep and surface approaches to problem solving and management, and the use of robotics to fa-cilitate deep learning strategies. Our analysis suggests that a combination of an open learning en-vironment, robotics as the learning tool, and learning-by-inventing provides a conducive envi-ronment for deep learning strategies, intrinsic motivation, and self-regulated learning, which are prerequisite condi-tions for creativity and inventing.