• Journal of the Korean earth science society
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• v.26 no.7
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• pp.630-639
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• 2005

The purpose of this study was to investigate the classifying ability of the igneous rocks with the naked eye for 36 preservice science teachers. For this, we selected six specimens of igneous rocks that consisted of rhyolite, andesite, basalt, granite, diorite, and gabbro, and performed the questionnaire with them. Preservice science teachers needed the average of 3 tools to classify the rocks. Most of the selected tools were loupe, streak plate, hammer and Mohs’ hardness scale. Many preservice science teachers selected basalt and granite samples to classify igneous rocks among 6 kinds of the rocks which were exhibited. However, the results of the identification with the naked eye showed that the right answer rate was significantly different based on what rock sample had been selected. Nobody gave the right answer among 10 students who chose the rhyolite sample, but all of 36 students who picked the basalt sample answered correctly. And $62\%$ of 8 students who chose the andesite sample, 62% of 32 student choosing granite, $7\%$ of 13 students choosing diorite and $44\%$ of 9 students choosing gabbro were correctly answered. In identifying igneous rock samples with the naked eye, most subjects relied on vesicular texture to basalt, and they used textural, color and empirical characters to granite. But, some felt more or less difficulty to distinguish between intermediate and light colors and to recognize porphyry.

• Journal of the Korean earth science society
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• v.35 no.6
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• pp.467-483
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• 2014

This study aimed to investigate the classification ability with naked eyes according to the understanding level about rocks of pre-service science teachers. We developed a questionnaire concerning misconception about minerals and rocks. The participants were 132 pre-service science teachers. Data were analyzed using Rasch model. Participants were divided into a master group and a novice group according to their understanding level. Seventeen rocks samples (6 igneous, 5 sedimentary, and 6 metamorphic rocks) were presented to pre-service science teachers to examine their classification ability, and they classified the rocks according to the criteria we provided. The study revealed three major findings. First, the pre-service science teachers mainly classified rocks according to textures, color, and grain size. Second, while they relatively easily classified igneous rocks, participants were confused when distinguishing sedimentary and metamorphic rocks from one another by using the same classification criteria. On the other hand, the understanding level of rocks has shown a statistically significant correlation with the classification ability in terms of the formation mechanism of rocks, whereas there was no statistically significant relationship found with determination of correct name of rocks. However, this study found that there was a statistically significant relationship between the classification ability with regard to formation mechanism of rocks and the determination of correct name of rocks.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.63-70
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• 2019

These days, as Interest in Image recognition with deep learning is increasing, there has been a lot of research in image recognition using deep learning. In this study, we propose a system for classifying rocks through rock images of 18 types of rock(6 types of igneous, 6 types of metamorphic, 6 types of sedimentary rock) which are addressed in the high school curriculum, using CNN model based on Tensorflow, deep learning open source framework. As a result, we developed a classifier to distinguish rocks by learning the images of rocks and confirmed the classification performance of rock classifier. Finally, through the mobile application implemented, students can use the application as a learning tool in classroom or on-site experience.