Student-Generated Content Improves Online Learning of Programming

Abstract

Quality learning content for online courses is difficult and time-consuming to create, and therefore students are increasingly employed to create learning content for use by their peers. Student-generated content however may be low quality and consequently of uncertain utility. The authors present a successful approach for improving online learning of programming through student-generated content – community tests that help other students in solving programming exercises. In their approach, the tests simplify student work on solving exercises, and the learning system can automatically provide focused help to each struggling student. They evaluated the proposed solution in an existing online learning system for programming in university programming courses. Results indicate that students used the proposed features often, and when examining the impact on learning performance, the active students showed improved performance compared to inactive students. Social awareness in online setting was evaluated using a questionnaire.