Towards modeling of human skilling for electrical circuitry using augmented reality applications

• Published in: International Journal of Educational Technology in Higher Education Vol. 18; no. 1; pp. 1 - 23
• Main Authors: Villanueva, Ana, Liu, Ziyi, Kitaguchi, Yoshimasa, Zhu, Zhengzhe, Peppler, Kylie, Redick, Thomas, Ramani, Karthik
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 21.06.2021; BioMed Central, Ltd; Springer Nature B.V; SpringerOpen
• Subjects: Alignment (Education); Augmented reality; Circuits; Classification; Cognitive Processes; Computer Appl. in Social and Behavioral Sciences; Computer Science; Computers and Education; Curricula; Curriculum Implementation; Definitions; Educational Assessment; Educational Benefits; Educational Facilities Improvement; Educational Objectives; Educational Principles; Educational Technology; Electrical circuitry; Electronic Equipment; Experiential Learning; Higher Education; Humanities; Influence of Technology; Informal Education; Information Systems Applications (incl.Internet); Instructional Design; Laboratory Equipment; Law; Learning; Mastery Learning; Microskills; Modeling; Modelling; Outcomes of Education; Prior Learning; Q-matrix; Quality assessment; Research Article; Segmentation; Simulated Environment; Skill Development; Skills; Statistics for Social Sciences; STEM Education; Student Motivation; Technical education; Use Studies; Skills; Circuits; Education; Q-matrix; Microskills; Electrical circuitry; Modeling; Knowledge space
• ISSN: 2365-9440; 2365-9440
• DOI: 10.1186/s41239-021-00268-9

Augmented reality (AR) is a unique, hands-on tool to deliver information. However, its educational value has been mainly demonstrated empirically so far. In this paper, we present a modeling approach to provide users with mastery of a skill, using AR learning content to implement an educational curriculum. We illustrate the potential of this approach by applying this to an important but pervasively misunderstood area of STEM learning, electrical circuitry. Unlike previous cognitive assessment models, we break down the area into microskills—the smallest segmentation of this knowledge—and concrete learning outcomes for each. This model empowers the user to perform a variety of tasks that are conducive to the acquisition of the skill. We also provide a classification of microskills and how to design them in an AR environment. Our results demonstrated that aligning the AR technology to specific learning objectives paves the way for high quality assessment, teaching, and learning.