Abstract
In this paper, the development of the MAR (mobile augmented reality) remedial teaching program is described. It allowed students to manipulate augmented objects through AURASMA app via internet and find leads to solve geometry problems regarding compound-cube-surface area. In order to foster students' spatial abilities, the program provided specific functions included partition, complementation, recombination, and multi-perspective to promote their “mental image” as well as “visualization.” The proposed program was evaluated with the quasi-experimental pre-test/post-test control group design to probe into students' learning performance. Moreover, students' error types of problem-solving were analyzed using their solving track recorded in the sheets. The results showed that the MAR remedial teaching program truly benefit students on solving geometry problems regarding compound-cube-surface area, and there are still some questions needed to investigate in the future, such as how students were affected by visual-obscuration to inexactly carry out their counts.
Highlights
After the publication of Hiebert’s edited book (1986), Mathematical knowledge had been recognized mostly in the form of conceptual and procedural knowledge (Putnam, 1986)
The results showed that the mobile-augmented reality (MAR) remedial teaching program truly benefit students on solving geometry problems regarding compoundcube-surface area, and there are still some questions needed to investigate in the future, such as how students were affected by visual-obscuration to inexactly carry out their counts
A bulk of research and theory in cognitive science supports the notions that deep understanding depends on how well a learner represents and connects bits of knowledge (Kilparick, Swafford & Findell, 2001)
Summary
After the publication of Hiebert’s edited book (1986), Mathematical knowledge had been recognized mostly in the form of conceptual and procedural knowledge (Putnam, 1986). Conceptual knowledge is connected web of knowledge and rich in relationships; procedural knowledge is composed of both the knowledge of mathematical symbols and the knowledge of algorithms or procedures that “step-by-step instructions that prescribe how to complete tasks” (Putnam, 1986). A bulk of research and theory in cognitive science supports the notions that deep understanding depends on how well a learner represents and connects bits of knowledge (Kilparick, Swafford & Findell, 2001). Relies mostly on how learners internalize the meaning related to a procedure they are learning or a concept that is being taught and connections made between them (Gulcin & Meral, 2015).
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