Predicting Learning Achievement Using Big Data Cluster Analysis : Focusing on Longitudinal Study

Ko, Su-Jeong

doi:10.9728/dcs.2018.19.9.1769

Cited by 4 publications

(2 citation statements)

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“…데이터는 장치-사물-사람 간의 초연결을 달성할 수 있는 유일한 프로토콜(protocol)이기 때문이다 (Ko, 2023). 빅 데이터의 특징 -4V's − 을 갖는 디지털 데이터는 전처리(preprocessing)의 적정성 여부가 그 유용 성을 결정하며, 적합한 분석 방법들의 체계적 적용이 그 효과성 − 올바른 정보와 지식의 도출 −을 결정한다 (Ko, 2022). 또한 DX 환경에서 고려되는 데이터는 특정 목적에 따라 계획되고 수집되는 통상적인 실험/조사에 기초하는 경우보다는 주로 시장, 고객 또는 생산 환경에서 소급 또는 관찰되는 특징을 갖는다.…”

Section: 서론unclassified

A study on rethinking EDA in digital transformation era

2024

KJAS

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Digital transformation refers to the process by which a company or organization changes or innovates its existing business model or sales activities using digital technology. This requires the use of various digital technologies -cloud computing, IoT, artificial intelligence, etc. -to strengthen competitiveness in the market, improve customer experience, and discover new businesses. In addition, in order to derive knowledge and insight about the market, customers, and production environment, it is necessary to select the right data, preprocess the data to an analyzable state, and establish the right process for systematic analysis suitable for the purpose. The usefulness of such digital data is determined by the importance of pre-processing and the correct application of exploratory data analysis (EDA), which is useful for information and hypothesis exploration and visualization of knowledge and insights. In this paper, we reexamine the philosophy and basic concepts of EDA and discuss key visualization information, information expression methods based on the grammar of graphics, and the ACCENT principle, which is the final visualization review standard, for effective visualization.

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Section: 서론unclassified

A study on rethinking EDA in digital transformation era

2024

KJAS

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“…Agnivesh et al applied clustering algorithm to data classi cation, which showed good performance and e ect [9]. Ko applied big data to the parallel improvement of clustering and concluded that clustering can be used in massive data classification [10]. On this basis, taking the alumni information in the school campus network as the basic data, and combined with the above research results, the current widely used hierarchical clustering method is improved and applied to analyze the alumni resources information; thus, the corresponding analysis model is constructed, which realizes the accurate identification and classification of alumni resources, so as to provide technical reference and research direction for the analysis of alumni information.…”

Section: Related Workmentioning

confidence: 99%

Construction of Alumni Information Analysis Model Based on Big Data

Wang

2022

Mathematical Problems in Engineering

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In order to integrate and utilize alumni resources in a better way, big data is utilized to construct alumni information analysis model based on improved hierarchical clustering algorithm, so as to realize mining and retrieval of alumni information. First,the basic principle of hierarchical clustering algorithm is analyzed concretely. Moreover, the improvement is performed on this basis, and a method of calculating the distance between class clusters based on the ant colony optimization is proposed, which uses the shortest distance of the ant colony algorithm to optimally solve the distance between hierarchical class clusters, so as to improve the clustering accuracy. Then, the alumni information analysis model based on improved hierarchical clustering algorithm is constructed, and the model is divided into text preprocessing, keyword extraction, text feature vector generation, name disambiguation, and alumni recognition modules. Finally, the improved hierarchical clustering algorithm and construction of model are verified by experiments. The results show that the accuracy of the improved agglomerative hierarchical clustering algorithm is as high as 86.4% on average and 3.8% and 4.8% more than the two traditional algorithms. Thus, the clustering effect of the algorithm is better, and the proposed alumni analysis model can effectively process text disambiguation of web pages and identification of alumni information, which has certain effectiveness.

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Analysis of Core Concepts in Problem Solving and Programming Unit of Informatics Subject Textbooks in Middle School Revised in 2015

Choe¹

2020

dcs

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Predicting Learning Achievement Using Big Data Cluster Analysis : Focusing on Longitudinal Study

Cited by 4 publications

References 10 publications

A study on rethinking EDA in digital transformation era

A study on rethinking EDA in digital transformation era

Construction of Alumni Information Analysis Model Based on Big Data

Analysis of Core Concepts in Problem Solving and Programming Unit of Informatics Subject Textbooks in Middle School Revised in 2015

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