• ETRI Journal
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• v.43 no.6
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• pp.1024-1037
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• 2021

Mining high utility itemsets (HUIs) from transaction databases considers such factors as the unit profit and quantity of purchased items. Two-phase tree-based algorithms transform a database into compressed tree structures and generate candidate patterns through a recursive pattern-growth procedure. This procedure requires a lot of memory and time to construct conditional pattern trees. To address this issue, this study employs two compressed tree structures, namely, Utility Count Tree and String Utility Tree, to enumerate valid patterns and thus promote fast utility computation. Furthermore, the study presents an algorithm called single-phase utility computation (SPUC) that leverages these two tree structures to mine HUIs in a single phase by incorporating novel pruning strategies. Experiments conducted on both real and synthetic datasets demonstrate the superior performance of SPUC compared with IHUP, UP-Growth, and UP-Growth+algorithms.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.306-312
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• 2021

The main goal of higher education institutions is to present a high level of quality education to its students. This study uses data mining techniques to extract educational data from cumulative databases and used them to make the right decisions. This paper also aims to find the factors affecting students' academic performance in Majmaah University, KSA, during 2010 - 2017 period. The study utilized a sample of 6,158 students enrolled from two colleges, males and females. The results showed a high percentage of stumbling and dismissed between graduate and regular students where more than 62.5% failed to follow the plan. Only 2% of students scored distinction during their study of all graduated since their grade point average, secondary level, was statistically significant, where p<0.05. Dismissed percentage was higher among males. These results promoted some recommendations in which decision-makers could take them in considerations for better improvement of academic achievements: including of specialized programs to follow-up in regards to stumbling and failure. Utilization of different communication tools are needed to activate academic advisory for dismiss and dropout evaluation.

• Korean Journal of Health Education and Promotion
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• v.26 no.4
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• pp.1-10
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• 2009

Objectives: The purpose of this study is to find the determinants of suicide impulse of residents living in mining region and other areas in one city. The past studies did not examine the suicide related attitudes or behaviors in mining region. This study also examines how coping resources and behaviors moderate the suicide impulse. Methods: For this purpose, hierarchical logistic regression method was used to predict the likelihood of suicide impulse. The personal characteristics, depression, coping resources and behaviors were considered as the independent variables. The data collected in this study was gathered through questionnaire survey with 502 residents in other areas as well as mining area in one city. Results and Conclusion: The results and conclusions are as follows: 1. The chi-square test revealed that residents living mining region showed higher percentage of suicide impulse compared to other areas. 2. The t-test revealed that those with suicide impulse had higher level of depression compared to those without it. This pattern was consistent in other areas as well as mining region. 3. The hierarchical logistic regression revealed that age, education, depression showed positive effect on suicide impulse in mining region. However, in other areas, education, illness, and depression showed positive effect on suicide impulse. Also, this result implies that suicide prevention efforts should be actively made in mining region.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.935-945
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• 2021

The purpose of this study is to analyze the social perception of Arduino lecture using big data analysis method. For this purpose, data from January 2012 to May 2021 were collected using the Textom website as a keyword searched for 'arduino + lecture' in blogs, cafes, and news channels of NAVER website. The collected data was refined using the Textom website, and text mining analysis and semantic network analysis were performed by opening the Textom website, Ucinet 6, and Netdraw programs. As a result of text mining analysis such as frequency analysis, TF-IDF analysis, and degree centrality it was confirmed that 'education' and 'coding' were the top keywords. As a result of CONCOR analysis for semantic network analysis, four clusters can be identified: 'Arduino-related education', 'Physical computing-related lecture', 'Arduino special lecture', and 'GUI programming'. Through this study, it was possible to confirm various meaningful social perceptions of the general public in relation to Arduino lecture on the Internet. The results of this study will be used as data that provides meaningful implications for instructors preparing for Arduino lectures, researchers studying the subject, and policy makers who establish software education or coding education and related policies.

• Educational Technology International
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• v.23 no.1
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• pp.97-127
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• 2022

With the recent advancement of technology, there has been an increase in professional development activities, including teachers using blogs to share practical knowledge and reflect on teaching and learning. This study was conducted to identify the contents of practical knowledge shared through the K-12 teachers' blogs. To achieve the research objective, 70,571 blog posts were collected from 329 blogs of K-12 teachers in Korean and analyzed using text mining techniques. The results of the study are as follows. First, practical knowledge sharing activities using teacher blogs have increased. Teachers posted a lot of blogs during the semester. Second, primary school teachers share various curriculum activities, reflections on project classes, class management, opinions related to education, and personal. Third, secondary school teachers share summaries and reviews of curriculum, materials related to college entrance exams, various instructional materials, opinions related to education, and personal experiences on their blogs. This study suggested that blogs are widely used as a venue for sharing practical knowledge of teachers, and that blogs can be a useful way to develop professionalism.

• Journal of the Korean Dietetic Association
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• v.23 no.2
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• pp.192-201
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• 2017

This study compared the Center for Children's Foodservice Management in 2012, 2014, and 2016 using big data and opinion mining. The data on the Center for Children's Foodservice Management were collected from the portal site, Naver, from January 1 to December 31 in 2012, 2014, & 2016 and analyzed by keyword frequency analysis, influx route analysis of data, polarity analysis via opinion mining, and positive and negative keyword analysis by polarity analysis. The results showed that nursery had the highest rank every year and education supported by Center for Children's Foodservice Management has increased significantly. The influx of data has increased through the influx route analysis of data. Blog and $caf\acute{e}e$, which have a considerable amount of information by the mother should be helpful for use as public relations and participation recruitment paths. By polarity analysis using opinion mining, the positive image of the Center for Children's Foodservice Management was increased. Therefore, the Center for Children's Foodservice Management was well-suited to the purpose and the interests of the people has been increasing steadily. In the near future, the Center for Children's Foodservice Management is expected have good recognition if various programs to participate with family are developed and advertised.

• The Journal of Korean Association of Computer Education
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• v.17 no.1
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• pp.117-130
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• 2014

The purpose of this study is to provide useful knowledge for improving indicators that represent competitiveness and educational competency of the university by deriving a new pattern or the meaningful results from the data of information disclosure of universities using statistical analysis and data mining techniques. To achieve this, a model of decision tree was made and various factors that affect education/research performance such as employment rate, the number of technology transfer and papers per full-time faculty were explored. In addition to this, the cluster analysis of universities was conducted using attributes related to evaluation of university. According to the analysis, common factors affecting higher education/research performance are following indicators ; incoming student recruitment rate, enrollment rate, and the number of students per full-time faculty. In the cluster analysis, when performed by the entire university, the size, location of the university respectively, clusters are mainly formed by well-known universities, art physical non-science and engineering religious leaders training universities, and others. The main influencing factors of this cluster are higher education/research performance indicators such as employment rate and the number of technology transfer.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.39-48
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• 2020

Data mining techniques have been suggested to find efficiently meaningful and useful information. Especially, in the big data environments, as data becomes accumulated in several applications, related pattern mining methods have been proposed. Recently, instead of analyzing not only static data stored already in files or databases, mining dynamic data incrementally generated in a real time is considered as more interesting research areas because these dynamic data can be only one time read. With this reason, researches of how these dynamic data are mined efficiently have been studied. Moreover, approaches of mining representative patterns such as maximal pattern mining have been proposed since a huge number of result patterns as mining results are generated. As another issue, to discover more meaningful patterns in real world, weights of items in weighted pattern mining have been used, In real situation, profits, costs, and so on of items can be utilized as weights. In this paper, we analyzed weighted maximal pattern mining approaches for data generated incrementally. Maximal representative pattern mining techniques, and incremental pattern mining methods. And then, the application scenarios for analyzing the required commodity patterns in infants are presented by applying weighting representative pattern mining. Furthermore, the performance of state-of-the-art algorithms have been evaluated. As a result, we show that incremental weighted maximal pattern mining technique has better performance than incremental weighted pattern mining and weighted maximal pattern mining.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.71-84
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• 2024

This paper presents a critical analysis of the current application of big data in higher education and how Learning Analytics (LA), and Educational Data Mining (EDM) are helping to shape learning in higher education institutions that have applied the concepts successfully. An extensive literature review of Learning Analytics, Educational Data Mining, Learning Management Systems, Informal Learning and Online Social Networks are presented to understand their usage and trends in higher education pedagogy taking advantage of 21st century educational technologies and platforms. The roles of and benefits of these technologies in teaching and learning are critically examined. Imperatively, this study provides vital information for education stakeholders on the significance of establishing a teaching and learning agenda that takes advantage of today's educational relevant technologies to promote teaching and learning while also acknowledging the difficulties of 21st-century learning. Aside from the roles and benefits of these technologies, the review highlights major challenges and research needs apparent in the use and application of these technologies. It appears that there is lack of research understanding in the challenges and utilization of data effectively for learning analytics, despite the massive educational data generated by high institutions. Also due to the growing importance of LA, there appears to be a serious lack of academic research that explore the application and impact of LA in high institution, especially in the context of informal online social network learning. In addition, high institution managers seem not to understand the emerging trends of LA which could be useful in the running of higher education. Though LA is viewed as a complex and expensive technology that will culturally change the future of high institution, the question that comes to mind is whether the use of LA in relation to informal learning in online social network is really what is expected? A study to analyze and evaluate the elements that influence high usage of OSN is also needed in the African context. It is high time African Universities paid attention to the application and use of these technologies to create a simplified learning approach occasioned by the use of these technologies.

• Journal of Engineering Education Research
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• v.9 no.4
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• pp.71-82
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• 2006

To accomplish the educational objectives of a department, a system for CQI(Continuous Quality Improvement) is necessary. Improving the educational system by survey analysis is one of the most important factors for accomplishing the educational objectives. In general, survey analysis is carried out by using statistical distribution on an attribute or correlation analysis between two attributes. However, these analysis schemes have a limitation that they cannot find relations among various attributes. In this paper, an in-depth survey analysis method applying data mining techniques is presented. Data mining is a technique for extracting interesting knowledges from a large set of data. Survey from undergraduate students in the School of Computing of Soongsil University is analyzed in this paper by using a data mining tool, called Clementine. Results of Clementine analysis show the relationship between 'grade', and other attributes hierarchically, and provide useful information that can be applied in student consulting and program improvement.