Data Mining Applications Used in Education Sector

Shrestha, S.; Pokharel, Manish

doi:10.3126/jer.v10i2.32721

Cited by 3 publications

(3 citation statements)

References 22 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…-The password of the specialty to which the student is admitted; -The student's admission ranking at the university; -Year of admission. These lessons are formed according to the specialty [41,42]. 4.…”

Section: Systemmentioning

confidence: 99%

See 1 more Smart Citation

Development of Knowledge Assessment Algorithm in E-Learning Environment

Ragimova Nazila Ali

2024

jes

View full text Add to dashboard Cite

The development of technology also manifests itself in the educational environment. The traditional education model is already being replaced by the modern online education model. An electronic learning environment is created here. Online education allows people to learn remotely. This is basically a pretty advanced feature for anyone with internet access. It is possible to get access to any classes at any time, regardless of time and place. E-learning, which has many advantages, also helps in the development of individual education. Electronic universities are one of the places where electronic educational environment is applied. E-universities can offer more advanced services to more students. At the same time, it is possible to choose an individual educational trajectory here. The selection of the individual trajectory of students is carried out on the basis of a certain method. And the management of electronic universities is carried out in electronic form. The article is devoted to the study of the electronic university project as an electronic educational environment and its main space. Online education, e-learning environment, e-universities, services offered by them, methods and means of implementing individual education, algorithm of e-university management, calculations and examples in this direction are mentioned here.

show abstract

Section: Systemmentioning

confidence: 99%

“…Registration date 3. Class Registration -Contains the classes the student has acquired and must complete for the new session.These lessons are formed according to the specialty[41,42]. 4.…”

mentioning

confidence: 99%

Development of Knowledge Assessment Algorithm in E-Learning Environment

Ragimova Nazila Ali

2024

jes

View full text Add to dashboard Cite

show abstract

“…The process of KDD applies Data Mining techniques [24] to discover knowledge by: 1. Selecting specific data sources as the Target Data.…”

Section: B Educational Data Miningmentioning

confidence: 99%

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

et al. 2022

View full text Add to dashboard Cite

Student retention is an essential measurement metric in education, indicated by retention rates, which are accumulated as students re-enroll from one academic year to the next. High retention rates can be obtained if institutions aim to provide appropriate support and teaching methods among the various practices to prevent students from deferring their studies. To address this pressing challenge faced by educational institutions, the underlying factors and the methodological aspects of building robust predictive models are reviewed and scrutinized. Educational Data Mining (EDM) and Learning Analytics (LA) have been widely adopted for knowledge discovery from educational data sources, improving the teaching practice, and identifying at-risk students. Various predictive techniques are applied in LA, such as Machine Learning (ML), Statistical Analysis, and Deep Learning (DL). To gain an in-depth review of these techniques, academic publications have been reviewed to highlight their potential to resolve Student Retention issues in education. Additionally, the paper presents a taxonomy of ML approaches and a comprehensive review of the success factors and the features that are not indicative of student performance in three different learning environments: Traditional Learning, Blended Learning, and Online Learning. The survey reveals that supervised ML and DL techniques are broadly applied in Student Retention. However, the application of ensemble and unsupervised learning clustering techniques supporting the heterogenous and homogenous groups of students is generally lacking. Moreover, static and traditional features are commonly used in student performance, ignoring vital factors such as educators-related, cognitive, and personal data. Furthermore, the paper highlights open challenges for future research directions.

show abstract

Öğrencilerin Akademik Performanslarının Tahmin Edilmesi için AutoML Tekniğinin Uygulanması

Aghalarova¹,

Keser²

2021

El-Cezeri Fen Ve Mühendislik Dergisi

View full text Add to dashboard Cite

Data Mining Applications Used in Education Sector

Cited by 3 publications

References 22 publications

Development of Knowledge Assessment Algorithm in E-Learning Environment

Development of Knowledge Assessment Algorithm in E-Learning Environment

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

Öğrencilerin Akademik Performanslarının Tahmin Edilmesi için AutoML Tekniğinin Uygulanması

Contact Info

Product

Resources

About