• Title/Summary/Keyword: Whole-task sequencing

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The Effects of Whole-task Sequencing Emphasis Manipulation on Expertise Acquisition in Web Based Complex Task (웹기반 복합적 과제에서 전체과제 계열화 강조변화 방법이 전문성 향상에 미치는 영향)

  • Kim, Kyung-Jin;Kim, Kyung
    • Journal of The Korean Association of Information Education
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    • v.20 no.6
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    • pp.629-644
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    • 2016
  • The purpose of this study was to investigate the effects of whole-task sequencing emphasis manipulation on expertise acquisition in web based complex task. To achieve the purpose, emphasis manipulation sequencing type is composed of a simple emphasis manipulation, a snowballing manipulation, and a full emphasis manipulation sequencing and participants was drawn from a pool of 93 undergraduate students sampled for the study. According to the findings, a snowballing manipulation group invested significantly lower cognitive load than a full emphasis manipulation group but did not a simple emphasis manipulation group. Based on these findings, though complex task is included of high interactivity owing to real task, learner cannot suffer cognitive overload because emphasis manipulation which can view the whole task and the part task in parallel provides meta cognition for learner. And whole-task sequencing emphasis manipulation affects to transfer. The snowballing emphasis manipulation group invested significantly higher than simple emphasis manipulation group and full emphasis manipulation group. Based on these findings, the snowballing manipulation which learner use whole-task sequencing and part-task sequencing simultaneously contribute to understandings and ability to solve problems for complex task and it will in turn, lead to expertise acquisition.

Improvement of Elementary Instruction via a Teacher Community: Focused on the Implementation of Five Practices for Orchestrating Productive Mathematics Discussions (교사 공동체를 중심으로 한 초등 수학 수업 개선: 효과적인 수학적 논의를 위한 5가지 관행의 적용)

  • Pang, Jeongsuk;Kim, Juhyeon;Choi, Yewon;Kwak, Eunae;Kim, Jeongwon
    • Education of Primary School Mathematics
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    • v.25 no.4
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    • pp.433-457
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    • 2022
  • An effective teacher community helps the participating teachers improve their instructional quality. This study reports a teacher community consisting of 15 elementary school teachers and one teacher educator. This paper analyzed 15 mathematics lessons in which the teachers implemented the five practices for orchestrating productive mathematics discussions by Smith and Stein (2018) based on the grade-specific discussions as well as the whole community's discussions. The results of this study showed that the overall levels of each practice either increased gradually or maintained at the highest Level 4, as mathematics lessons had been implemented. Specifically, the following practices were quite successful: setting goals for a lesson, selecting an appropriate task, anticipating student responses, and selecting student solutions. However, both sequencing and connecting student solutions were implemented at various levels. Monitoring student work tended to remain at Level 2 which included incorrect implementation of the practice. This paper closes with implications related to the skillful implementation of the five practices through a teacher community.

Design and Implementation of Memory-Centric Computing System for Big Data Analysis

  • Jung, Byung-Kwon
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.7
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    • pp.1-7
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    • 2022
  • Recently, as the use of applications such as big data programs and machine learning programs that are driven while generating large amounts of data in the program itself becomes common, the existing main memory alone lacks memory, making it difficult to execute the program quickly. In particular, the need to derive results more quickly has emerged in a situation where it is necessary to analyze whether the entire sequence is genetically altered due to the outbreak of the coronavirus. As a result of measuring performance by applying large-capacity data to a computing system equipped with a self-developed memory pool MOCA host adapter instead of processing large-capacity data from an existing SSD, performance improved by 16% compared to the existing SSD system. In addition, in various other benchmark tests, IO performance was 92.8%, 80.6%, and 32.8% faster than SSD in computing systems equipped with memory pool MOCA host adapters such as SortSampleBam, ApplyBQSR, and GatherBamFiles by task of workflow. When analyzing large amounts of data, such as electrical dielectric pipeline analysis, it is judged that the measurement delay occurring at runtime can be reduced in the computing system equipped with the memory pool MOCA host adapter developed in this research.