Abstract
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.
Highlights
New technologies, such as augmented reality (AR), which superimposes virtual information into the physical world, provide unique hands-on capabilities to deliver educational content
In order to systematize the process of breaking down, aligning, and improving the AR content, we propose the use of Q-matrix theory, a cognitive assessment approach which evaluates the associations between questions/steps in a task and the microskills— smallest segmentation of knowledge—required to complete it (Tatsuoka, 1995, 2009)
A Tukey post hoc test revealed that the average score—out of 10 points, which represents 10 microskills—for the PartialAR was statistically higher for the second session
Summary
New technologies, such as augmented reality (AR), which superimposes virtual information into the physical world, provide unique hands-on capabilities to deliver educational content. An AR display (e.g., a headset, a tablet or a mobile phone) provides the user with an interface to the virtual world, which enables interactions with physical objects. This virtual information is overlaid on objects and can demonstrate to users what cannot be perceived by their own senses and learned (Iftene & Trandabăt, 2018) (e.g., pressure, temperature, voltage).
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