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Virtual labs in engineering education: Modeling perceived critical mass of potential adopter teachers

Publication Type : Journal Article

Thematic Areas : Learning-Technologies

Publisher : Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),

Source : Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8095 LNCS, pp. 288-300, 2013

Url : http://www.scopus.com/inward/record.url?eid=2-s2.0-84883350301&partnerID=40&md5=3d2a2f930ea6dc8377a48a1951dfb5e9

ISBN : 9783642408137

Keywords : Critical mass, Experiments, Innovation characteristics, Innovation diffusion, Laboratories, Multimedia systems, Multimedia technologies, Science experiments, simulation, Technology innovation, Virtual lab

Campus : Amritapuri

School : School of Engineering, Centre for Cybersecurity Systems and Networks, Department of Computer Science and Engineering

Center : AmritaCREATE, Amrita Center For Research in Analytics, Cyber Security

Department : Computer Science, cyber Security

Year : 2013

Abstract : Virtual labs for science experiments are a multimedia technology innovation. A possible growth pattern of the perceived critical mass for virtual labs adoption is modeled using (N=240) potential-adopter teachers based on Roger's theory of diffusion and of perceived attributes. Results indicate that perceived critical mass influences behavior intention to adopt a technology innovation like Virtual Labs and is affected by innovation characteristics like relative advantage, ease of use and compatibility. The work presented here models the potential-adopter teacher's perceptions and identifies the relative importance of specific factors that influence critical mass attainment for an innovation such as Virtual Labs.

Cite this Research Publication : Raghu Raman, Dr. Krishnashree Achuthan, and Prof. Prema Nedungadi, “Virtual labs in engineering education: Modeling perceived critical mass of potential adopter teachers”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8095 LNCS, pp. 288-300, 2013

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