How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

Journal of Information Systems Engineering & Management, Journal Year: 2023, Volume and Issue: 8(2), P. 21168 - 21168

Published: April 27, 2023

In the last decade, artificial intelligence (AI), machine learning (ML) and data analytics have been introduced with great effect in field of higher education. However, despite potential benefits for education institutions (HIE´s) these emerging technologies, most them are still early stages adoption technologies. Thus, a systematic literature review (SLR) on published over 5 years applications is necessary. Following PRISMA guidelines, out 1887 initially identified SCOPUS-indexed publications topic, 171 articles were selected review. To screen abstracts titles each citation, Rayyan QCRI was used. VOSViewer, software tool constructing visualizing bibliometric networks, Microsoft Excel used to generate charts figures. The findings show that widely researched application ML related prediction academic performance employability students. implications will be invaluable researchers practitioners explore how AI technologies ,in era ChatGPT, can universities without jeopardizing integrity.

Language: Английский

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Classification Techniques Using Machine Learning for Graduate Student Employability Predictions

International Journal on Advanced Science Engineering and Information Technology, Journal Year: 2024, Volume and Issue: 14(1), P. 45 - 56

Published: Feb. 6, 2024

The issue of employability has gained significant importance, not only for graduate students but also higher educational institutions. In this regard, prediction models using machine learning have emerged as crucial techniques assessing students' potential to secure employment after graduation. Enhancing university is critical because student unemployment a global concern that widespread negative effects on both individuals and Therefore, focusing predictions considered essential in addressing issue. Traditionally, demographic academic attributes, such CGPA, been key factors determining status. However, research suggests various other factors, satisfaction, might influence employability. This study employs identify the affect objective investigate features significantly influencing ability employment. Data was collected from Malaysia's Ministry Education's tracer (SKPG). Several classification algorithms were applied, including Logistic Regression, Random Forest, Naïve Bayes, Support Vector Machine, Extreme Gradient Boosting, Artificial Neural Networks (ANN). results show ANN achieved highest accuracy, with around 80%. findings revealed satisfaction level facilities (e.g., library counseling service) are predictions. Consequently, empirical can help institutions enhance prepare necessary skills future

Language: Английский

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Customized support vector machine for predicting the employability of students pursuing engineering

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: 16(5), P. 3193 - 3204

Published: April 6, 2024

Language: Английский

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Assessing the impact of digital education and the role of the big data analytics course to enhance the skills and employability of engineering students

Frontiers in Psychology, Journal Year: 2022, Volume and Issue: 13

Published: Oct. 19, 2022

This study aims to explore the role of digital education in development skills and employability for engineering students through researching big data analytics courses. The empirical proposes hypothesis that both soft hard have positive effects on human capital, individual attributes, career dimensions students. is achieved constructing a framework three students’ two competency A questionnaire survey was conducted with 155 college structural equation model (SEM) used test hypotheses. results found courses impact abilities ( p < 0.01) 0.001) dimensions, while more significant employability. has practical theoretical implications further enriches knowledge base broadens our understanding digitalization enhancing

Language: Английский

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Employability Prediction of Information Technology Graduates using Machine Learning Algorithms

International Journal of Advanced Computer Science and Applications, Journal Year: 2022, Volume and Issue: 13(10)

Published: Jan. 1, 2022

The ability to predict graduates' employability match labor market demands is crucial for any educational institution aiming enhance students' performance and learning process as the metric of success higher education (HEI). Especially information technology (IT) graduates, due evolving demand IT professionals increased in current era. Job mismatch unemployment remain major challenges issues institutions various factors that influence needs. Therefore, this paper aims introduce a predictive model using machine (ML) algorithms demands. Five classification were applied named Decision tree (DT), Gaussian Naïve Bayes (Gaussian NB), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM). dataset used study collected based on survey given graduates employers. evaluated terms accuracy, precision, recall, f1 score. results showed DT achieved highest second accuracy was by LR SVM.

Language: Английский

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A study on employment sustainability among Engineering students using a Statistical and Deep Learning framework

Published: May 5, 2024

In today's saturated job market, it has been one of the biggest challenges for fresh engineering graduates from India to secure a satisfactory offer. this study, Statistical and Deep learning (SDL) framework is proposed predict students' academic achievement in terms employability. The feature selection part carried out with help statistical module. A Learning (DL) module employed their Cumulative Grade Point Average (CGPA) leading sustainable employment. DL based on Convolutional Neural Network (CNN) attention based, stacked Bidirectional Long Short-Term Memory (BiLSTM). Lastly, improve interpretability framework, an explainability incorporated. findings demonstrate how well suggested forecasts employability over long run areas which work be done make them employable as soon they graduate. features Binomial distribution yields lowest Mean Squared Error (MSE) Absolute (MAE) its superior accuracy Gaussian Poisson distributions.

Language: Английский

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The impact of internship experience on the employability of vocational students: a bibliometric and systematic review

Cogent Business & Management, Journal Year: 2024, Volume and Issue: 11(1)

Published: Aug. 10, 2024

Employability is a primary priority for vocational students in the competitive labor market, as it essential to meet demands of globalization and Fourth Industrial Revolution. However, there still limited research on trends developments, well systematic reviews available this topic. This study aims examine develop conceptual framework related impact internship experiences employability, including its dimensions indicators. 23 articles published between 2009 2023 Scopus database, which appeared 15 international journals. The was analyzed using bibliometric review methods, employing Biblioshiny VOSviewer software. findings indicate that highest publication trend occurred 2022, with 5 articles. most productive country United States, total 229 documents. Meanwhile, cited journal Education Training from Emerald Group Publishing. author citations Irwin, Ami, 220 citations. developing topics, based thematic map, are student satisfaction, experiential learning, employers, programs, experience impacting employability students. implications will assist stakeholders, industries, education, government, researchers policy formulation curriculum development relevant needs market.

Language: Английский

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Integrating Intelligent Web Scraping Techniques in Internship Management Systems: Enhancing Internship Matching

Annals of Emerging Technologies in Computing, Journal Year: 2025, Volume and Issue: 9(1), P. 1 - 23

Published: Jan. 1, 2025

The study explores the integration of intelligent web scraping techniques to enhance internship matching process within management systems. increasing demand for internships necessitates timely and efficient intern matching, a task that conventional manual need help with due its complexity time-consuming nature. Intelligent algorithms machine learning analyze extensive datasets match interns businesses based on competencies, interests, professional objectives. leverages natural language processing extract relevant information from listings candidate profiles, enhancing precision effectiveness process. Additionally, clustering refine recommendations, pairing students opportunities fit their competencies career However, implementing raises ethical concerns, particularly regarding data privacy algorithmic bias. Ensuring utilization these is critical fair unbiased matching. research addresses considerations while proposing framework integrating into existing reviews literature in management, critically analyzing synthesizing past findings demonstrate efficacy over methods. also introduces theoretical model effective investigating optimize it examines benefits, challenges, limitations techniques. proposed approach simplifies aligns student strengths opportunities, enhances onboarding efficiency, bridges academic practical application.

Language: Английский

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The influence employability of vocational students through internship experiences and 21st-century competencies: a moderated mediation model

Cogent Education, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 11, 2025

Language: Английский

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Placement prediction and skill gap analysis using machine learning model

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3279, P. 020144 - 020144

Published: Jan. 1, 2025

Language: Английский

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