The purpose of this study was to design a teaching method suitable for science high school students using atomic force microscopy. During their scientific inquiry procedure, high school students observed a micro-nanostructure of a biological sample, which is unobservable via an optical microscope. The developed teaching method enhanced students' science-learning motivation and scientific creativity.

The purpose of this study was to analyze task commitment types of science learning in high school students' biology classification. Thirty students were selected as the representatives of five task commitment types according to task commitment type inventory scores. They participated in think-aloud biology classification task. To analyze the procedural characteristics of task commitment, a coding scheme and think-aloud task were developed. Characteristics of respective task commitment types were identified from the result of the think-aloud protocol coding analysis. They are TGC(task goal commitment) type, LGC(low goal commitment) type, CC(conditional commitment) type, SC(suspended commitment) type, and DC(delayed commitment) type. Findings gained from this study are expected to serve as the foundation of task commitment enhancement strategies and as the information on the characteristics of each task commitment type. Also, future studies are required to investigate the commitmentrelated properties not only in biology classification but also in other science learning situations. : task commitment type, science learning, task solving process, biology classification, think-aloud

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Categorization is an important human function used to process different stimuli. It is also one of the most important factors affecting measurement of a person's classification ability. Explicit categorization, the representative system by which categorization ability is measured, can verbally describe the categorization rule. The purpose of this study was to develop a prediction model for categorization ability as it relates to the classification process of living organisms using fMRI.Fifty-five participants were divided into two groups: a model generation group, comprised of twenty-seven subjects, and a model verification group, made up of twenty-eight subjects. During prediction model generation, functional connectivity was used to analyze temporal correlations between brain activation regions. A classification ability quotient (CQ) was calculated to identify the verbal categorization ability distribution of each subject. Additionally, the connectivity coefficient (CC) was calculated to quantify the functional connectivity for each subject. Hence, it was possible to generate a prediction model through regression analysis based on participants' CQ and CC values. The resultant categorization ability regression model predictor was statistically significant; however, researchers proceeded to verify its predictive ability power. In order to verify the predictive power of the developed regression model, researchers used the regression model and subjects' CC values to predict CQ values for twenty-eight subjects. Correlation between the predicted CQ values and the observed CQ values was confirmed.Results of this study suggested that explicit categorization ability differs at the brain network level of individuals. Also, the finding suggested that differences in functional connectivity between individuals reflect differences in categorization ability. Last, researchers have provided a new method for predicting an individual's categorization ability by measuring brain activation.

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