Prediction of Students Performance Level Using Integrated Approach of ML Algorithms

Purpose: For learning environments like schools and colleges, predicting students' success is one of the most important issues since it aids in the creation of practical systems that, among other things, promote academic achievement and prevent dropout. They gain from the automation of several student-related tasks that deal with large amounts of data gathered via software tools for technology-enhanced learning. Theoretical framework: A rigorous analysis and processing of this data might provide us with knowledge about the students' knowledge and how it relates to academic assignments. In this study, classroom management, teacher qualification and in-service training with the effect so mediating variable of teacher quality have been used to measure students’ performance. Design/methodology/approach: 364 respondents have been participated by using questionnaire are data collection methods and the data are analyses by using SMART-PLS. Findings: The findings of the study revealed that the classroom management on students’ performance in-service training, and teacher qualification have significant relationship with teacher quality. The strength and the relationships of the variables might be re-examined by the future researchers, including adding more variables from strategic, organizational, and environmental dimensions to determine the accuracy of the model. Research, Practical & Social implications: This study contributes to the existing body of knowledge in understanding the school quality management as this is the first study with the set of such variables. In other words, this study attempts to increase the understanding of the relationship between school quality management strategies on students’ performance. Originality/value: Education authorities should encourage all teachers to develop themselves through regular participation in seminars and conferences organized in their field of expertise in order to update their knowledge and acquire relevant teaching skills to help them impart appropriate knowledge to their students. In addition, the education department should provide schools with basic teaching equipment to make the teaching and learning process easier in schools.

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“…Academic circles do not have a clear definition of the behavior of students' classroom performance (Baig et. al., 2023;Hafdi et.…”

Section: Students' Performancementioning

confidence: 99%

Factors Affecting Students Performance Mediated by Teachers Quality in Higher Vocational Colleges in Maoming City, China

Qingyan¹,

Azar²,

Ahmad³

2023

J. Professional Business Review

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“…In the context of higher education, the prediction and understanding of student performance considers a significant process that leads to achieving academic goals and enhancing the overall quality of education. in addition, it allows the institute to support individuals and students at risk within an early stage in a timely manner, This leads to better learning experiences and overall improvement of educational efficacy [3]. Therefore, this paper proposes a unique approach to classify student performance using an ensemble model with a stacking classifier.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Student'sPerformance Classification Using Ensemble Modeling

Nafea,

Mishlish,

haban Shaban

et al. 2023

Iraqi Journal for Computer Science and Mathematics

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A precise prediction of student performance is an important aspect within educational institutions toimprove results and provide personalized support of students. However, the predication accuracy of studentperformance considers an open issue within education field. Therefore, this paper proposes a developed approachto identify performance of students using a group modeling. This approach combines the strengths of multiplealgorithms including random forest (RF), decision tree (DT), AdaBoosts, and support vector machine (SVM).Afterward, the last ensemble estimates as one of the bets logistic regression methods was utilized to create a robustand reliable predictive model because it considers The experiments were evaluated using the Open UniversityLearning Analytics Dataset (OULAD) benchmark dataset. The OULAD dataset considers a comprehensive datasetcontaining various characteristics related to the student’s activities thereby five cases based on the utilized datasetwere investigated. The experiment results showed that the proposed ensemble model presented its ability withaccurate results to classify student performance by achieving 95% of accuracy rate. As a result, the proposed modelexceeded the accuracy of individual basic models by using the strengths of various algorithms to improve thegeneralization by reducing the potential weaknesses of individual models. Consequently, the education institutescan easily identify students who may need additional support and interventions to improve their academicperformance.

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Prediction of Academic Outcomes Using Machine Learning Techniques: A Survey of Findings on Higher Education

Valdiviezo-Diaz,

Chicaiza

2024

Communications in Computer and Information Science

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Prediction of Students Performance Level Using Integrated Approach of ML Algorithms

Cited by 8 publications

References 10 publications

Factors Affecting Students Performance Mediated by Teachers Quality in Higher Vocational Colleges in Maoming City, China

Factors Affecting Students Performance Mediated by Teachers Quality in Higher Vocational Colleges in Maoming City, China

Enhancing Student'sPerformance Classification Using Ensemble Modeling

Prediction of Academic Outcomes Using Machine Learning Techniques: A Survey of Findings on Higher Education

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