Large class teaching with Backstage

Bry, François and Pohl, Alexander Yong-Su (2017): Large class teaching with Backstage. In: Journal of Applied Research in Higher Education, Vol. 9, No. 1: pp. 105-128

Abstract

Purpose - Several challenges of today's higher education were motivations to reconsider the contents and formats of lectures and tutorials and to conceive the classroom communication system Backstage, a social media platform supporting a novel form of large-class teaching. The purpose of this paper is to report on the challenges met, on the novel teaching form and on an evaluation of this teaching form. Design/methodology/approach - The use of Backstage in two courses is evaluated. One of the courses has been specially adapted to promote student participation, the other course has been held in a traditional way. To investigate the usefulness and acceptance of Backstage in the given settings the data collected on Backstage and student responses in surveys are analyzed. Findings - The results indicate that Backstage can foster interactivity and awareness in large-class lectures when used in combination with a teaching format that provides opportunities for and encourges lecture-relevant communication. Furthermore, students appreciated the use of Backstage. Research limitations/implications - This paper reports on a case study which lacks generalizability. Further studies under controlled conditions and of the learning effectiveness of the approach are still outstanding. Practical implications - This paper describes an approach fostering a form of Active Learning in large classes. Since large classes are widespread in higher education, the approach has a considerable practical potential. Social implications - The paper describes an approach to large class higher education teaching in using social media. Originality/value - Similar results have not been published so far.

• Item Type: Journal article
• Faculties: Mathematics, Computer Science and Statistics > Computer Science
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• ISSN: 2050-7003
• Language: English