Predicting University's Students Performance Based on Machine Learning Techniques

Ahmed, Dindar Mikaeel; Abdulazeez, Adnan Mohsin; Zeebaree, Diyar Qader; Ahmed, Falah Y. H.

doi:10.1109/i2cacis52118.2021.9495862

Cited by 19 publications

(9 citation statements)

References 43 publications

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“…This is a quick synopsis of machine learning using decision trees. A decision tree is a type of hierarchical tree structure in which a decision or testing on a characteristic is represented by each node, the test result is represented by each branch, and the ultimate decision or goal variable is represented by each leaf node [32].…”

Section: A Random Forestmentioning

confidence: 99%

Facial Beauty Standards Predictions Based on Machine Learning: A Comparative Analysis

Sadiq,

Abdulazeez

2024

ijcs

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This study uses a variety of machine learning and classification methods to anticipate the Facial Beauty Standards. The Accuracy of five different models—Random Forest, Logistic Regression, Support Vector Machine (SVM), KNN, and decision tree—were used to analyses each one. There were noticeable differences in the models' performances. In particular, the Logistic Regression and SVM methods demonstrate almost perfect accuracy, followed closely by random forest and KNN. This study gives insight into how well different models perform in comparison and emphasizes the benefits and drawbacks of each in terms of predicting face beauty standards.

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Section: A Random Forestmentioning

confidence: 99%

Facial Beauty Standards Predictions Based on Machine Learning: A Comparative Analysis

Sadiq,

Abdulazeez

2024

ijcs

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“…Ahmed et al [15] proposed an approach to predict university students' performance in final exams using the algorithm (GBDT) which is a machine learning tech-nology used for regression, classification, and ranking tasks, and is part of the Boosting method family.…”

Section: Related Workmentioning

confidence: 99%

Early Predicting of Students Performance in Higher Education

Alhazmi

Sheneamer

2023

IEEE Access

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Students learning performance is one of the core components for assessing any educational systems. Students performance is very crucial in tackling issues of learning process and one of the important matters to measure learning outcomes. The ability to use data knowledge to improve education systems has led to the development of the field of research known as educational data mining (EDM). EDM is the creation of techniques to investigate data gathered from educational settings, allowing for a more thorough and accurate understanding of students and the improvement of educational outcomes for them. The use of machine learning (ML) technology has increased significantly in recent years. Researchers and teachers can use the measurements of success, failure, dropout, and more provided by the discipline of data mining in education to predict and simulate education processes. Therefore, this work presents an analysis of students performance using data mining methods. The paper presents both clustering and classification techniques to identify the impact of students performance at early stage with on the GPA. For the clustering technique, the paper uses dimensionality reduction mechanism by T-SNE algorithm with various factors at early stage such as admission scores and first level courses, academic achievement tests (AAT) and general aptitude tests (GAT) in order to explore the relationship between these factors and GPA's. For the classification technique, the paper presents experiments on different machine learning models on predicting student performance at early stages using different features including courses' grades and admission tests' scores. We use different assessment metrics to evaluate the quality of the models. The results suggest that educational systems can mitigate the risks of students failures at the early stages.

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“…In many prior EDM and LA studies, researchers have employed a type of ML algorithm, known as Naïve Bayes, to forecast student performance in various STEM settings (see Shahiri and Husain, 2015;Ahmed et al, 2021;Perez and Perez, 2021 for examples). In recent systematic literature reviews, Shafiq et al (2022), Peña-Ayala (2014), and Baashar et al (2021), found that Naïve Bayes was used in 35%, 20%, and 14% of education studies surveyed, respectively.…”

Section: Application To Stem Educational Settings and ML Assessmentmentioning

confidence: 99%

An application of Bayesian inference to examine student retention and attrition in the STEM classroom

Bertolini

Finch²,

Nehm³

2023

Front. Educ.

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IntroductionAs artificial intelligence (AI) technology becomes more widespread in the classroom environment, educators have relied on data-driven machine learning (ML) techniques and statistical frameworks to derive insights into student performance patterns. Bayesian methodologies have emerged as a more intuitive approach to frequentist methods of inference since they link prior assumptions and data together to provide a quantitative distribution of final model parameter estimates. Despite their alignment with four recent ML assessment criteria developed in the educational literature, Bayesian methodologies have received considerably less attention by academic stakeholders prompting the need to empirically discern how these techniques can be used to provide actionable insights into student performance.MethodsTo identify the factors most indicative of student retention and attrition, we apply a Bayesian framework to comparatively examine the differential impact that the amalgamation of traditional and AI-driven predictors has on student performance in an undergraduate in-person science, technology, engineering, and mathematics (STEM) course.ResultsInteraction with the course learning management system (LMS) and performance on diagnostic concept inventory (CI) assessments provided the greatest insights into final course performance. Establishing informative prior values using historical classroom data did not always appreciably enhance model fit.DiscussionWe discuss how Bayesian methodologies are a more pragmatic and interpretable way of assessing student performance and are a promising tool for use in science education research and assessment.

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Predicting University's Students Performance Based on Machine Learning Techniques

Cited by 19 publications

References 43 publications

Facial Beauty Standards Predictions Based on Machine Learning: A Comparative Analysis

Facial Beauty Standards Predictions Based on Machine Learning: A Comparative Analysis

Early Predicting of Students Performance in Higher Education

An application of Bayesian inference to examine student retention and attrition in the STEM classroom

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