Predicting the Suitability of IT Students' Skills for the Recruitment in Saudi Labor Market

Alghamlas, Munirah; Alabduljabbar, Reham

doi:10.1109/cais.2019.8769577

Cited by 15 publications

(13 citation statements)

References 11 publications

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“…Alghamlas and Alabduljabbar conducted survey among bachelor's degree IT Students from Saudi Arabia universities and three months statistics poised from two online job gateways (LinkedIn and Bayt.com). This study indorses maximum desirable IT skills in the Saudi Arabia's employment market (Alghamlas and Alabduljabbar 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, their prediction window size is the shortest possible as models are used for fresh graduates. In Alghamlas and Alabduljabbar (2019), a web-based application is proposed designed to predict the suitability of IT students 'skills for employment in the Saudi labor market and to provide a set of general recommendations regarding skills and competencies required to anticipate and prevent unemployment crisis. Their prediction window size is less than a year.…”

Section: Foreseeing Long Term Market Demandmentioning

confidence: 99%

“…Accordingly, surveys were the second option for data acquisition, it allowed the collection of data in a structured way (Mishra et al 2016;Bharambe et al 2017;Tapado et al 2018;Dubey and Mani 2019). Further, hybrid data acquisition method was considered where questionnaires' data and academic records were merged to build the dataset (Alghamlas and Alabduljabbar 2019;Casuat et al 2020).…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Despite the importance of the preprocessing phase, some works do not provide enough details about applied techniques (Piad et al 2016;Bharambe et al 2017;Othman et al 2018;Alghamlas and Alabduljabbar 2019;Kumar 2019;Patel et al 2020;Mewburn et al 2020). A detailed description of preprocessing practices in the related literature is presented in Table 3.…”

Section: Data Preparationmentioning

confidence: 99%

See 3 more Smart Citations

Employability prediction: a survey of current approaches, research challenges and applications

Mezhoudi

Alghamdi

Aljunaid

et al. 2021

J Ambient Intell Human Comput

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Student employability is crucial for educational institutions as it is often used as a metric for their success. The job market landscape, however, more than ever dynamic, is evolving due to the globalization, automation, and recent advances in Artificial Intelligence. Identifying the significant factors affecting employability, as well as the requirements of the new job market can tremendously help all stakeholders. Knowing their weaknesses and strengths, students might better plan their career. Instructors can focus on more appropriate skill sets to meet the requirements of rapidly evolving labor markets. Program managers can anticipate and improve their curriculum to build new competencies, both for educating, training and reskilling current and future workers. All these combined efforts certainly can contribute to increasing employability. Data driven and machine learning techniques have been extensively used in various fields of educational data mining. More and more studies are investigating data mining techniques for the prediction of employability. Yet, these studies show a lot of variation, for instance, with respect to the data used, the methods adopted, or even the research questions posed. In this paper, we aim to depict a clear picture of the art, clarifying for each standard step of data mining process, the differences, and similarities of these studies, along with further suggestions. Thus, this survey provides a comprehensive roadmap, enabling the application of data mining for employability.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Foreseeing Long Term Market Demandmentioning

confidence: 99%

Section: Data Acquisitionmentioning

confidence: 99%

Section: Data Preparationmentioning

confidence: 99%

See 2 more Smart Citations

Employability prediction: a survey of current approaches, research challenges and applications

Mezhoudi

Alghamdi

Aljunaid

et al. 2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…An ANN with two-layer feed forward network that uses Backpropagation algorithm is created for predicting the users who had given with a definite job offer. In [6] they collected data related to Information Science skills, self-regulated learning (SRL), and academic accomplishment of Information Science students and developed a classification model using Nearest Neighbor Naïve Bayes, Decision Tree and algorithms to predict the aptness of Information Science graduates to the Saudi industry. In [7] they compared five algorithms under n different classification technique on Information Technology employability dataset.…”

Section: Related Workmentioning

confidence: 99%

Employability Prediction of Engineering Graduates using Machine Learning Algorithms

K*,

H K

2020

IJRTE

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Number of graduates produced in each year by higher education institutions is increasing. Thus prediction of employability of graduate’s plays a vital role for any industry for proper talent acquisition and Utilization and also it helps students in identifying the qualification and skills that they need to improve, before completion of degree to get desired jobs. In this Digital Revolution, informal learning and skill enhancements is happening in unconditional method, relating and converging all this learning’s to the employability rate is one of a biggest issue. The main objective is to address this issue by predicting and forecasting the skill acquisition continuously and mapping to industry needs using machine learning Algorithms. The proposed work used different machine learning algorithms like Logistic Regression, Decision tree, k-nearest neighbor, Support Vector Machine and Naïve Bayes for building model where ANN classifier resulted with the highest accuracy of 87.42%. This research would be helpful for all the organization including government, Private and corporations, including students and teachers for employability prediction.

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Recruitment Data Analysis Using Machine Learning in R Studio

Devakunchari¹,

Anand²,

Vedhanayaki³

et al. 2019

Inventive Computation Technologies

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Predicting the Suitability of IT Students' Skills for the Recruitment in Saudi Labor Market

Cited by 15 publications

References 11 publications

Employability prediction: a survey of current approaches, research challenges and applications

Employability prediction: a survey of current approaches, research challenges and applications

Employability Prediction of Engineering Graduates using Machine Learning Algorithms

Recruitment Data Analysis Using Machine Learning in R Studio

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