The Most Efficient Classifiers for the Students’ Academic Dataset

Al-Fairouz, Ebtehal Ibrahim; Abdullah, Mohammed Amin

doi:10.14569/ijacsa.2020.0110960

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Linear discrimination analysis approach is the most accurate [12] 2020 Virtual Assistants Allowing students to have virtual assistants to help guide them throughout their learning. [13], [14] 2020…”

Section: Machine Learning Classification Modelsmentioning

confidence: 99%

“…This is a helpful tool for educators to use when trying to figure out how to best help students who are struggling academically. New patterns and insights can be discovered [13] to identify Int J Artif Intell ISSN: 2252-8938  students who may be at risk and take appropriate safeguards to help them succeed [9], which also may be valuable for the decision-makers and higher administrative level. On the other hand, blockchain is a cutting-edge technology that will aid in the advancement of education.…”

Section: Benefits Of the Blockchain And Machine Learning Adoption In ...mentioning

confidence: 99%

“…Al-Fairouz and Al-Hagery [13], [14] employed various machine learning algorithms to analyze the student's academic data as educational data at Qassim University, In the Faculty of Economics and Management. The objective was to find out new unseen patterns with insights and identify the drawbacks and difficulties of the analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blockchain and machine learning in education: a literature review

Alhabeeb,

Alrusayni,

Almutiri

et al. 2024

IJ-AI

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<span lang="EN-US">There is a growing influence around the use of technology in education, with many solutions already being implemented and many others being explored. Using various forms of technology to assist the educational process has increased dramatically in the previous decade in education systems in many respects. Both machine learning and the blockchain have had a significant impact on education. The purpose of this study is to conduct a literature review on the application of machine learning and blockchain technology in educational institutions. Additionally, this study examines the potential applications, benefits, and challenges those educational institutions may face as a result of using machine learning and blockchain technologies. Using machine learning and blockchain in educational systems will have a positive impact on the entire educational process and student achievement. Researchers, academics, and practitioners will benefit from this study to focus on a wider range of educational applications and solve the related issues of machine learning and blockchain technology in the education sector.</span>

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Section: Machine Learning Classification Modelsmentioning

confidence: 99%

Section: Benefits Of the Blockchain And Machine Learning Adoption In ...mentioning

confidence: 99%

See 1 more Smart Citation

Blockchain and machine learning in education: a literature review

Alhabeeb,

Alrusayni,

Almutiri

et al. 2024

IJ-AI

View full text Add to dashboard Cite

show abstract

“…Although in many fields, such as references 8 and 9, the highest levels of accuracy have been achieved in a majority of the classifiers, the ensemble classifiers are successful, especially in the text classification. In the literature, many extraction approaches have been developed, including BOW, TF-IDF, and word2vec, for the ensemble classification.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel ensemble approach for heterogeneous data with active learning

Salama

Abdelkader

Abdelwahab

2022

International Journal of Engineering Business Management

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At present, millions of internet users are contributing a huge amount of data. This data is extremely heterogeneous, and so, it is hard to analyze and derive information from this data that is considered an indispensable source for decision-makers. Due to this massive growth, the classification of data and analysis has become an important research subject. Extracting information from this data has become a necessity. As a result, it was necessary to process these enormous volumes of data to uncover hidden information and therefore improve data analysis and, in turn, classification accuracy. In this paper, firstly, we focus on developing an ensemble machine-learning model based on active learning which identifies the most effective feature extraction strategy for heterogeneous data analysis, and compare it with traditional machine-learning algorithms. Secondly, we evaluate the proposed model during the experiments; five heterogeneous datasets from various domains were used, such as a Health Care Reform dataset, Sander Frandsen dataset, Financial Phrase Bank dataset, SMS Spam Collection dataset, and Textbook sales dataset. According to the results, the novel approach for data analysis performed better than conventional methods. Finally, the study’s findings confirmed the validity of the suggested technique, meeting the study’s goal of using ensemble methods with active learning to raise the model’s overall accuracy for effectively classifying and analyzing heterogeneous data, reducing the time and money spent training the model, and delivering superior analysis performance as well as insights into other elements of extracting information from heterogeneous data.

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“…Reference [16] focuses on the use of planning programs to analyze students' performance in finding hidden patterns, which aid officials in making important educational decisions. Data mining procedures were applied to retrieve hidden information from student data, and comparisons were made between three dividers, Nave Bayes, Decision Tree and Random Forest.…”

Section: Related Workmentioning

confidence: 99%

Model for Prediction of Student Grades using Data Mining Algorithms

Shibli

Abri

Sunny³

et al. 2022

COMPUTE

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There has been a rapid growth in the educational domain since education has become an important need. Data is collected in this domain which can be put to meaningful use to derive a lot of benefits to the students. Predicting student performance can help students and their teachers keep track of student progress. Mining Educational data helps to uncover invisible patterns, relationships, or trends in the unstructured data and helps in delivering logical and meaningful recommendations. Several kinds of research are being conducted across the world to analyze the data regarding student learning to identify the factors affecting performance and to provide support to students to help them improve. It is the objective of the proposed research to conduct a detailed study in the Sultanate of Oman regarding the existing toolsets, systems, and mode of data collection that are used currently in the Education sector for the prediction of Student Grades. Taking this as the baseline, later a model that will feature different prediction algorithms which are more accurate in predicting the grades of a student will be developed. The objective of this research is to understand the various predictive methods used to predict student performance and to propose a machine learning model to predict student grades.

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The Most Efficient Classifiers for the Students’ Academic Dataset

Cited by 6 publications

References 18 publications

Blockchain and machine learning in education: a literature review

Blockchain and machine learning in education: a literature review

A novel ensemble approach for heterogeneous data with active learning

Model for Prediction of Student Grades using Data Mining Algorithms

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