• Journal of The Korean Association For Science Education
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• v.39 no.3
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• pp.405-414
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• 2019

As a case study, we analyzed the professor's teaching professionalism in college of science-engineering based on pedagogical content knowledge. Two professors from the college of science-engineering located in Chungcheongbuk-do participated in this study. After collecting their syllabi, we observed their classes and conducted semi-structured interview before and after the classes. Analysis were conducted on their syllabi, taped videos, field notes and transcripts of the interview. As a result, both professors' orientation to teaching was not only related to learning subject matter knowledge, but also developing job competencies or communication skills. Their orientations were transformed according to the educational situation. After establishing the final learning goals, the professors organized weekly plan by listing the topics. However, they were unaware that the goals of each topic should be presented in detail in the class. Also, one of them presented a lack of understanding of the students' prior knowledge and showed low understanding of what aspects of the topics make students difficult to learn. In addition, the two professors applied various teaching strategies such as giving examples, questioning, and problem solving, but the degree of students' participation was not enough in the process of teaching. In the aspect of assessment, the professors had low understanding in students' achievement or feedback. Based on the results of the analysis, this study proposed methods and strategies for improving the teaching professionalism of science-engineering professors.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.54-66
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• 2008

A spatio-temporal join is an expensive operation that is commonly used in spatio-temporal database systems. In order to generate an efficient query plan for the queries involving spatio-temporal join operations, it is crucial to estimate accurate selectivity for the join operations. Given two dataset $S_1,\;S_2$ of discrete data and a timestamp $t_q$, a spatio-temporal join retrieves all pairs of objects that are intersected each other at $t_q$. The selectivity of the join operation equals the number of retrieved pairs divided by the cardinality of the Cartesian product $S_1{\times}S_2$. In this paper, we propose aspatio-temporal histogram to estimate selectivity of spatio-temporal join by extending existing geometric histogram. By using a wide spectrum of both uniform dataset and skewed dataset, it is shown that our proposed method, called Spatio-Temporal Histogram, can accurately estimate the selectivity of spatio-temporal join. Our contributions can be summarized as follows: First, the selectivity estimation of spatio-temporal join for discrete data has been first attempted. Second, we propose an efficient maintenance method that reconstructs histograms using compression of spatial statistical information during the lifespan of discrete data.