A Hybrid Infrastructure of Enterprise Architecture and Business Intelligence &amp; Analytics for Knowledge Management in Education

Moscoso-Zea, Oswaldo; Castro, Jorge Washington Valarezo; Paredes-Gualtor, Joel; Luján-Mora, Sergio

doi:10.1109/access.2019.2906343

Cited by 38 publications

(23 citation statements)

References 41 publications

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“…In addition, a case study of experimental design was presented to improve the quality of teaching at a university in Seoul, South Korea. This study quantitatively and qualitatively analyzed the factors affecting the teaching quality of teachers and selected two factors that could be controlled by teachers and three factors that could not be controlled by teachers for analysis, so as to explore the best teaching methods [ 7 – 9 ]. The results showed that maintaining a close relationship with students at the beginning of the course and holding a bi-monthly learning session were most effective.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Impact Assessment of Big Data on Higher Education Management Based on Time‐Varying Clustering Sampling Algorithm

Liu

Song

2021

Computational Intelligence and Neuroscience

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With the continuous development of social economy, education has received more and more attention. As an important part of higher education, it has been profoundly influenced by the era of data in recent years. In order to evaluate the impact of big data on higher education management, this paper introduced a time-varying lens algorithm (TLA) to analyze the business needs of teachers and education management by sorting out the business processes of education management, education thinking, and education practice. Through learning and mining the corresponding technology, improve the corresponding educational ability, and use big data to assist the management of higher education. The simulation results show that the time-varying clustering sampling algorithm is effective.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Impact Assessment of Big Data on Higher Education Management Based on Time‐Varying Clustering Sampling Algorithm

Liu

Song

2021

Computational Intelligence and Neuroscience

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“…ere are a lot of matters involved in enterprise lean management quality management, such as courses and evaluation index, etc. Association rules can extract valuable knowledge hidden in data, assist educational administrators to make effective decisions, and improve the management level [19][20][21].…”

Section: Quality Evaluation Of Enterprise Lean Managementmentioning

confidence: 99%

Application of Data Mining in Effect Evaluation of Lean Management

Ding¹,

Li²,

2022

Scientific Programming

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Quantitative evaluation is an important part of enterprise diagnosis, which promotes the scientific and modern management of enterprises. At present, the existing enterprise management evaluation methods cannot complete the mining of enterprise index data, which leads to large error and low significance coefficient in enterprise management evaluation. Therefore, the application of data mining in enterprise lean management effect evaluation is put forward. The process and main functions of data mining are analyzed; data mining algorithm is used to establish the evaluation index system of lean management effect and calculate the index weight. Using the association rules method in data mining, according to the parameters of enterprise lean management level evaluation index and weight value, through the fuzzy set transformation idea, the fuzzy boundary of each index and factor is described by the membership degree, the fuzzy judgment matrix is constructed, and the final evaluation result is obtained by multilayer compound calculation. Experimental results show that this study has a high significance coefficient, and the proposed evaluation method of enterprise lean management effect has ideal accuracy and short time consumption. In practical application, the cumulative contribution rate is higher and has higher stability.

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“…The perception of the usefulness of KMS, the existence of IT infrastructure, financial support, and proper training in using the system are some of the most important factors to facilitate its adoption among potential users (Oumran et al, 2021). Moscoso-Zea et al (2019) apply KMSs in HEIs, presenting what they described as "a hybrid information infrastructure for business intelligence and analytics". Their goal was to implement a KMS based on educational data warehouses for analytics and data visualization.…”

Section: Knowledge Management In Heismentioning

confidence: 99%

Managing Knowledge About Future of Work: A Model for Higher Education Institutions

Argôlo¹,

Miranda²,

Pagliusi³

et al. 2022

ECKM

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The Fourth Industrial Revolution is causing considerable changes to the world of work. The interaction between technology and work, which takes many forms such as digitalization, automation, and augmentation, is happening quickly and broadly, impacting economic sectors left almost untouched by previous industrial revolutions. In this scenario, Higher Education Institutions (HEIs) must be able to foresee changes to prepare future professionals to match the needs of this new digital age. The professionals that are being prepared today need to learn a new set of skills related to emerging and disruptive technologies, such as Artificial Intelligence, the Internet of Things, and Big Data. In this paper, we propose a Knowledge Management model to help manage the HEIs’ teaching staff knowledge about the future of work, specifically, the expected skills and competencies to be highly demanded from professionals in the future. Therefore, we performed a brief review of related work about Knowledge Management in the context of HEIs, Management of Future-oriented Knowledge, and the application of the Delphi method to studies concerning the Future of Work. Considering this previous work, we propose a Knowledge Management model that combines the European Foundation for Quality Management (EFQM) Excellence Model framework with a Delphi process that is used during the Knowledge Generation step of the Knowledge Management process. The proposed model considers that professors are experts in their areas of concentration and, as such, are capable of helping their HEIs with their knowledge that can be used to improve the courses’ curricula. The model also considers that HEIs can help professors make this knowledge explicit, then store, transfer, and apply it. We provide detailed information about how to apply the model, how to deal with potential application problems and the model limitations. The proposed Knowledge Management model can help HEIs to keep up with the trends of demands of the labor market.

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A Hybrid Infrastructure of Enterprise Architecture and Business Intelligence & Analytics for Knowledge Management in Education

Cited by 38 publications

References 41 publications

[Retracted] Impact Assessment of Big Data on Higher Education Management Based on Time‐Varying Clustering Sampling Algorithm

[Retracted] Impact Assessment of Big Data on Higher Education Management Based on Time‐Varying Clustering Sampling Algorithm

Application of Data Mining in Effect Evaluation of Lean Management

Managing Knowledge About Future of Work: A Model for Higher Education Institutions

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