Predicting Student Employability Through the Internship Context Using Gradient Boosting Models

Saidani, Oumaima; Menzli, Leila Jamel; Ksibi, Amel; Alturki, Nazik; Alluhaidan, Ala Saleh

doi:10.1109/access.2022.3170421

Cited by 23 publications

(15 citation statements)

References 52 publications

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“…They identified important predictive features that impact employability using gradient-boosting classifiers. The internship feature showed high significance in affecting employability status providing insights to students and the concerned authorities to review the curriculum (Saidani, et al, (2022) [19]. In the thesis by Hugo (2018) [20] the researcher's objective is to predict the employability of undergraduate business students.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review on the Employability Prediction Model for the Management Students

Crasta

Shailashri

2023

IJCSBE

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Purpose: The demand for management education is on a constant surge that has resulted in quality check of the students who are employed and will be of value to the organisation and country at large. This paper attempts to explore the literature that will help in understanding the students’ employability based on attributes that are vital to both industry and academia. The employability prediction model will help the management to understand the students’ output quality in terms of being employable or not. It will also help them to improve employability through proper intervention by training and development programs. Methodology: The study is based on the literature of employability models that have been developed using various approaches especially that of machine learning. The published research papers in various journals that were reviewed range from the time period 2006 -2022 have been analysed using ABCD analysis. Findings/Result: The employability models have been developed using machine learning approaches. The necessity of an accurate employability prediction model that serves as a guide to help students and faculty is the need of the hour. Originality Value: Employability models are of great value to the students and institutions as they provide a silver lining in knowing the students’ employability. There are several gaps in terms of building an appropriate model that will serve as a scale to measure the uncertainty that exists in knowing the quality of output produced by management educational institutions. Type of Paper: Review paper.

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Section: Related Workmentioning

confidence: 99%

A Systematic Review on the Employability Prediction Model for the Management Students

Crasta

Shailashri

2023

IJCSBE

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“…In recent years, many researchers attempted to use machine learning in higher education to enhance graduate's features and curricula to support employability [8]. To discuss the contribution of ML in continuous quality improvement.…”

Section: Related Workmentioning

confidence: 99%

“…A brief description of each is described below: Accuracy: It is a common metric for evaluating classifier performance. It computes the ratio of correctly classified instances to the total number of instances [8]. Its formula is as follows: www.ijacsa.thesai.org Accuracy = TP+TN TP+FP+TN+FN (4) Precision: is the ratio of true positive instances divided by the total number of instances predicted as positive [22].…”

Section: Model Evaluationmentioning

confidence: 99%

Employability Prediction of Information Technology Graduates using Machine Learning Algorithms

ElSharkawy¹,

Helmy²,

Yehia³

2022

IJACSA

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

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“…Meanwhile, the Gradient Boosting Decision Tree (GBDT) algorithm can optimize the model by using an additive model and a forward step algorithm ( 42 ). Some scholars have used GBDT algorithm to effectively predict the employability of graduates in the internship environment ( 43 ). A European study effectively predicted the impact of psychosocial factors on quality of life in older adults people through machine learning algorithms ( 44 ).…”

Section: Introductionmentioning

confidence: 99%

Effective analysis of job satisfaction among medical staff in Chinese public hospitals: a random forest model

Li,

Meng

2024

Front. Public Health

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ObjectiveThis study explored the factors and influence degree of job satisfaction among medical staff in Chinese public hospitals by constructing the optimal discriminant model.MethodsThe participant sample is based on the service volume of 12,405 officially appointed medical staff from different departments of 16 public hospitals for three consecutive years from 2017 to 2019. All medical staff (doctors, nurses, administrative personnel) invited to participate in the survey for the current year will no longer repeat their participation. The importance of all associated factors and the optimal evaluation model has been calculated.ResultsThe overall job satisfaction of medical staff is 25.62%. The most important factors affecting medical staff satisfaction are: Value staff opinions (Q10), Get recognition for your work (Q11), Democracy (Q9), and Performance Evaluation Satisfaction (Q5). The random forest model is the best evaluation model for medical staff satisfaction, and its prediction accuracy is higher than other similar models.ConclusionThe improvement of medical staff job satisfaction is significantly related to the improvement of democracy, recognition of work, and increased employee performance. It has shown that improving these five key variables can maximize the job satisfaction and motivation of medical staff. The random forest model can maximize the accuracy and effectiveness of similar research.

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Predicting Student Employability Through the Internship Context Using Gradient Boosting Models

Cited by 23 publications

References 52 publications

A Systematic Review on the Employability Prediction Model for the Management Students

A Systematic Review on the Employability Prediction Model for the Management Students

Employability Prediction of Information Technology Graduates using Machine Learning Algorithms

Effective analysis of job satisfaction among medical staff in Chinese public hospitals: a random forest model

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