Students Performance: From Detection of Failures and Anomaly Cases to the Solutions-Based Mining Algorithms

Al-Fairouz, Ebtehal Ibrahim; Al-Hagery, Mohammed Abdullah

doi:10.37624/ijert/13.10.2020.2895-2908

Cited by 7 publications

(7 citation statements)

References 26 publications

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“…Similarly, [6,[16][17][18][19]24,75] converge in their predictions on higher education data using classifiers such as Random Forest (RF), SVM, Neural Networks and decision trees. Likewise, linear regression or logistic regression was used to obtain predictive models that detect failure, success, or academic performance early enough [1,81], or in turn, semi-supervised learning to obtain patterns in students who managed to pass the courses for a university degree [22]. Being the main objective to achieve very attractive and reliable accuracies, undoubtedly, accuracy always comes hand in hand with the quantity and quality of the data.…”

Section: Discussionmentioning

confidence: 99%

Predicting Academic Success of College Students Using Machine Learning Techniques

Guanin-Fajardo,

Guaña-Moya,

Casillas

2024

Data

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College context and academic performance are important determinants of academic success; using students’ prior experience with machine learning techniques to predict academic success before the end of the first year reinforces college self-efficacy. Dropout prediction is related to student retention and has been studied extensively in recent work; however, there is little literature on predicting academic success using educational machine learning. For this reason, CRISP-DM methodology was applied to extract relevant knowledge and features from the data. The dataset examined consists of 6690 records and 21 variables with academic and socioeconomic information. Preprocessing techniques and classification algorithms were analyzed. The area under the curve was used to measure the effectiveness of the algorithm; XGBoost had an AUC = 87.75% and correctly classified eight out of ten cases, while the decision tree improved interpretation with ten rules in seven out of ten cases. Recognizing the gaps in the study and that on-time completion of college consolidates college self-efficacy, creating intervention and support strategies to retain students is a priority for decision makers. Assessing the fairness and discrimination of the algorithms was the main limitation of this work. In the future, we intend to apply the extracted knowledge and learn about its influence of on university management.

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Section: Discussionmentioning

confidence: 99%

Predicting Academic Success of College Students Using Machine Learning Techniques

Guanin-Fajardo,

Guaña-Moya,

Casillas

2024

Data

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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%

“…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%

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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“…This paper presented a study of the tools used in EDM and a review of current EDM trends in which related work strategies in the field are compared. Reference [10] In this study, they used the Orange data mining platform as an open source software for data mining and machine learning. Data mining strategies include Linear Regression, Rules Association, Naive 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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Students Performance: From Detection of Failures and Anomaly Cases to the Solutions-Based Mining Algorithms

Cited by 7 publications

References 26 publications

Predicting Academic Success of College Students Using Machine Learning Techniques

Predicting Academic Success of College Students Using Machine Learning Techniques

Blockchain and machine learning in education: a literature review

Model for Prediction of Student Grades using Data Mining Algorithms

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