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Design and the technology acceptance model analysis of instructional mapping
Author(s) -
Dai Zhicheng,
Wang Mengting,
Liu Sannyuya,
Tang Liqiong
Publication year - 2020
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.22261
Subject(s) - technology acceptance model , computer science , incentive , knowledge management , instructional design , mathematics education , cloud computing , resource (disambiguation) , software , service (business) , empirical research , psychology , multimedia , usability , human–computer interaction , computer network , philosophy , economy , epistemology , programming language , economics , microeconomics , operating system
As a simple and effective tool of information integration, mind mapping can facilitate students' epistemic efficiency and improve teachers' pedagogical content knowledge, which can solve the problems being encountered in the reform of Chinese universities. To establish a systematic instructional method for mind mapping, this paper proposed a new instructional mode based on mind mapping and developed instructional mapping (IM), a classroom teaching software based on the starC system. The starC system is a cloud learning platform, which integrates learning resource, intelligent management, online community, and third‐party service. The starC system has been in use in 16 smart classrooms of Central China Normal University since 2017. However, it seems that many teachers prefer traditional instructional modes than IM. To determine the factors that can encourage teachers to accept IM, we carried out an empirical study based on the technology acceptance model (TAM). The external variables were school factors and student factors. School factors included evaluation and incentive systems. Student factors included students' interest in IM and their evaluation of teachers' use of IM. The empirical study was validated using survey data from 110 teachers. The findings in the TAM analysis revealed significant evidence in favor of the adoption of IM and provided theoretical and practical implications for teachers, school administrators, and instructional mode researchers and developers.