scholarly journals Investigating factors affecting library visits by university students using data mining

2018 ◽  
Vol 28 (1) ◽  
Author(s):  
Puarungroj Wichai ◽  
Pongpatrakant Pathapong ◽  
Boonsirisumpun Narong ◽  
Phromkhot Suchada
Keyword(s):  
Data Mining ◽  
University Students ◽  
Factors Affecting ◽  
Using Data

Detection of Desertion Patterns in University Students Using Data Mining Techniques: A Case Study

2018 ◽  
pp. 420-429
Author(s):  
Dayana Vila ◽  
Saúl Cisneros ◽  
Pedro Granda ◽  
Cosme Ortega ◽  
Miguel Posso-Yépez ◽  
...  
Keyword(s):  
Data Mining ◽  
University Students ◽  
Data Mining Techniques ◽  
Using Data

Quality Control of Micro-Drilled Hole Wall of PWBs Using Data-Mining

2005 ◽  
Author(s):  
Eiichi Aoyama ◽  
Toshiki Hirogaki ◽  
Keiji Ogawa ◽  
Tsuyoshi Otsuka ◽  
Katsutoshi Yamauchi
Keyword(s):  
Data Mining ◽  
Surface Roughness ◽  
Present Report ◽  
Weft Yarn ◽  
Hole Drilling ◽  
Printed Wiring Boards ◽  
Factors Affecting ◽  
Hole Wall ◽  
Using Data ◽  
Through Hole

In the manufacturing of printed wiring boards (PWBs), various methods have been developed in order to improve the circuit packaging density. Micro-drills are generally used to make smaller diameter through-holes in PWBs, which are desired for the miniaturization of equipment. However, a problem has emerged in that copper plating degraded by hole drilling can reduce the reliability of the electrical connection between layers. The surface roughness of drilled hole wall is one of the important factors affecting the plating quality. The purpose of the present report is to apply data-mining to the surface roughness data of drilled through-hole walls, and to elucidate the factors required to control the drilled hole quality. The following conclusions were obtained. (1) The data-mining aided by a computer was found to be effective to control the drilled hole wall quality in the PWBs manufacturing. (2) It was clear that the surface roughness of drilled hole walls depended on three factors: the drill temperature, cutting distance, and the width of the fiber bundle of weft yarn.

The Study of Score Analysis for Secondary School Based on Data Mining Technology

2011 ◽  
Vol 219-220 ◽  
pp. 396-399
Author(s):  
Shang Fu Hao ◽  
Zhi Qiang Zhang ◽  
Ying Hui Wei
Keyword(s):  
Data Mining ◽  
Score Distribution ◽  
Mining Technology ◽  
Factors Affecting ◽  
Architecture Model ◽  
Scientific Teaching ◽  
Score Analysis ◽  
Secondary Teaching ◽  
Using Data ◽  
Analysis System

Nowadays, the contents associated with deep score analysis is rarely involved in the existing secondary teaching management software, which is not conductive to fully develop the information implied by these data,without scientific teaching evaluation. Using data mining technology, multiple aspects of student score distribution will be shown accurately, identifying the regular factors affecting score changes. Standard score as the mathematical model is adopted in the system, choosing the standard SOA architecture model, and a scientific and efficient score analysis system based on JAVA, JSP is developed. The system provides decision support information for academic departments to promote better teaching work, and finally improve the quality of teaching.

scholarly journals Analysis of the factors affecting the iron ore pellet FEO index using data mining technique

2020 ◽  
Vol 4 (2) ◽  
pp. 83-92
Author(s):  
Mahdi Nakhaeinejad ◽  
Farzaneh Zarei
Keyword(s):  
Data Mining ◽  
Iron Oxide ◽  
Optimal Range ◽  
Data Mining Technique ◽  
Factors Affecting ◽  
Five Factors ◽  
Regression Algorithms ◽  
Iron Ore Pellet ◽  
Using Data ◽  
The Ideal

One of the most critical factors affecting iron pellet quality is the reduction in FeO (Iron Oxide) index in the final product. This study aims to predict factors affecting the FeO (Iron Oxide) of iron pellets and find out the contribution of each factor to reduce the pellets FeO (the ideal amount is between 0.4 to 0.6) using data mining tech­niques. When the FeO index's value is in the optimal range, the quality and price of pellets are higher. The data used in this study was collected from the pelletizing plant of Gol-E-Gohar in Sirjan, Iran, and the decision tree and regression algorithms are used in this analysis. Forty-five factors that can affect the FeO (Iron Oxide) index of the final product were studied, showing that the Magnesium Oxide and Airflow of the inlet fan of the indurating machine had the greatest impact on the FeO (Iron Oxide) of iron pellets.

scholarly journals Data Mining Methods for Detecting the Most Significant Factors Affecting Students’ Performance

2020 ◽  
Vol 12 (5) ◽  
pp. 1-13
Author(s):  
Mohammed Abdullah Al-Hagery ◽  
◽  
Maryam Abdullah Alzaid ◽  
Tahani Soud Alharbi ◽  
Moody Abdulrahman Alhanaya
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Study Finding ◽  
Vital Role ◽  
Educational Institutions ◽  
Factors Affecting ◽  
Educational Environments ◽  
Important Field ◽  
Using Data ◽  
Relationship Of

The field of using Data Mining (DM) techniques in educational environments is typically identified as Educational Data Mining (EDM). EDM is rapidly becoming an important field of research due to its ability to extract valuable knowledge from various educational datasets. During the past decade, an increasing interest has arisen within many practical studies to study and analyze educational data especially students’ performance. The performance of students plays a vital role in higher education institutions. In keeping with this, there is a clear need to investigate factors influencing students’ performance. This study was carried out to identify the factors affecting students’ academic performance. K-means and X-means clustering techniques were applied to analyze the data to find the relationship of the students' performance with these factors. The study finding includes a set of the most influencing personal and social factors on the students’ performance such as parents’ occupation, parents’ qualification, and income rate. Furthermore, it is contributing to improving the education quality, as well as, it motivates educational institutions to benefit and discover the unseen patterns of knowledge in their students' accumulated data.

scholarly journals A Proposed Model to Identify Factors Affecting Asthma using Data Mining

2019 ◽  
Vol 27 (1) ◽  
pp. 203-212
Author(s):  
Marjan Ghazisaeedi ◽  
Abbas Sheikhtaheri ◽  
Nasrin Behniafard ◽  
Fatemehalsadat Aghaei Meybodi ◽  
Rouhallah khara ◽  
...  
Keyword(s):  
Data Mining ◽  
Factors Affecting ◽  
Proposed Model ◽  
Using Data

scholarly journals Prediction of Academic Dropout in University Students Using Data Mining: Engineering Case

2020 ◽  
pp. 495-500
Author(s):  
Jesús Silva ◽  
Luisa Fernanda Arrieta Matos ◽  
Claudia Medina Mosquera ◽  
Carlos Vargas Mercado ◽  
Rosio Barrios González ◽  
...  
Keyword(s):  
Data Mining ◽  
University Students ◽  
Mining Engineering ◽  
Using Data

scholarly journals Exploring Technical Quality Factors That Enhance Mobile Learning Applications Services Using Data Mining Techniques

2021 ◽  
Vol 17 (4) ◽  
pp. 1-23
Author(s):  
Ahmad Abu-Al-Aish
Keyword(s):  
Data Mining ◽  
Student Satisfaction ◽  
Mobile Learning ◽  
Interface Design ◽  
Teaching And Learning ◽  
New Technologies ◽  
Quality Factors ◽  
Factors Affecting ◽  
Technical Factors ◽  
Using Data

Mobile learning (m-learning) has become an increasingly attractive solution for schools and universities that utilize new technologies in their teaching and learning setting. This study investigates the technical factors affecting the development of m-learning applications services from students’ perspectives. It presents a model consisting of twelve technical factors, including content usefulness, scalability, security, functionality, accessibility, interface design, interactivity, reliability, availability, trust, responsiveness, and personalization. To evaluate the model, a questionnaire was designed and distributed to 151 students in Jerash University, Jordan. The results indicate that all technical factors have positive affects on learner satisfaction and overall m-learning applications services, however the data mining analysis revealed that security and scalability factors exert a major impact on student satisfaction with m-learning applications services. This study gives insight for the future of developing and design m-learning applications.

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