[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123100-en":3,"doc-seo-123100-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123100,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting Post-Internship Employability Using Ensemble Machine Learning Approach","Graduate employability plays a decisive role for both students and higher education institutions, yet academic performance alone only partially explains job outcomes. This study assesses how internship-related variables affect employability after internship, including duration, training performance, and prior work experience. Using a machine learning framework with Universiti Malaysia Sarawak student records from 2019–2021, multiple algorithms are evaluated via feature selection and repeated K-fold cross-validation. Stacking delivers the best accuracy at 91%, with internship duration and training performance emerging as key predictors, highlighting the value of structured internship programs.","Journal of Cognitive Sciences and Human Development. Vol.10(2), September 2024  \nPredicting Post-Internship Employability Using Ensemble Machine  \nLearning Approach  \nAiza Azlin binti Kahlik & Abdulrazak Yahya Saleh*  \nFaculty of Cognitive Science and Human Development, Universiti Malaysia Sarawak, Sarawak,  \nMalaysia.  \nABSTRACT  \nGraduate employability is crucial for both students and higher education institutions. While academic performance has traditionally been a key predictor of employability, its predictive power is limited, necessitating the exploration of additional factors influencing post-internship job placement. This study investigates the impact of internship-related variables on graduate employability, such as duration, training performance, and prior work experience. Employing a machine learning approach on a dataset comprising student records from Universiti Malaysia Sarawak spanning from 2019 to 2021, we compared the performance of various algorithms, including ensemble methods. Feature selection and repeated K-fold cross-validation optimised model performance. Results indicate that stacking outperforms traditional models, achieving an accuracy of 91%. Particularly, internship duration and training performance emerged as significant predictors of employability. These findings underscore the importance of robust internship programs in enhancing graduate outcomes. Future research could explore the competencies developed during internships and their correlation with job success.  \nKeywords: graduate employability, machine learning, internship, career readiness, employability prediction, ensemble methods  \nARTICLE INFO  \nEmail [address: ysahabdulrazak@unimas.my](address: ysahabdulrazak@unimas.my) (Abdulrazak Yahya Saleh)  \n*Corresponding author  \n[https://doi.org/10.33736/jcshd.7518.2024](https://doi.org/10.33736/jcshd.7518.2024)  \ne-ISSN: 2550-1623  \nManuscript received: 1 August 2024; Accepted: 5 September 2024; Date of publication: 30 September 2024  \nCopyright: This is an open-access article distributed under the terms of the CC-BY-NC-SA (Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License), which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.  \nJournal of Cognitive Sciences and Human Development. Vol.10(2), September 2024  \n1 INTRODUCTION  \nThe transition from academia to the professional world is a complex journey fraught with challenges, particularly in securing employment (Tamrat, 2023; Webb et al., 2022) . Despite increasing higher education attainment rates, youth unemployment remains a persistent global issue (Nisha & Rajasekaran, 2018) . This phenomenon underutilises human capital and erodes economic growth and societal stability (Herbert et al., 2020) . As a result, higher education institutions (HEIs) have increasingly emphasised employability as a core outcome, seeking to equip graduates with the necessary skills and competencies to thrive in the job market (Hassock & Hill, 2022) . Given the perceived importance of internships, it is no surprise that the number of bachelor's degree holders who have undergone an internship during their studies has been increasing in proportion and absolute numbers, as shown in Figure 1. In 2010, 51,293 bachelors, or 69.0%, had gone for an internship. By 2019, that number had doubled to 106,502 graduates, making up a proportion of 88.4% of all bachelors who have undergone an internship.  \nFigure 1. Number and proportion of bachelors by internship status, 2010-2019 (Ministry of  \nHigher Education, Malaysia (2021)) .  \nInternships have emerged as a pivotal strategy to bridge the gap between theoretical knowledge and practical experience (Baker & Fitzpatrick, 2022; Rogers et al., 2021) . Beyond theoretical knowledge acquisition, internships provide valuable exposure to real-world work environments (Kim et al., 2022; Oberman et ","cbCairgcrIqjjefs","https://ap.wps.com/l/cbCairgcrIqjjefs","pdf",258226,1,15,"English","en",105,"# Abstract\n# Introduction\n## Employability as an education outcome\n## Role and growth of internships\n## Research gap and motivation","[{\"question\":\"Why does the study focus on post-internship employability beyond academic performance?\",\"answer\":\"Academic performance has limited predictive power for employability, motivating exploration of additional internship-related factors that better explain job placement after internship.\"},{\"question\":\"Which internship variables are examined as predictors of employability?\",\"answer\":\"The study evaluates internship-related variables including duration, training performance, and prior work experience.\"},{\"question\":\"What model approach performs best and what accuracy does it achieve?\",\"answer\":\"Stacking outperforms traditional models, reaching an accuracy of 91% after feature selection and repeated K-fold cross-validation.\"}]","Predicting Post-Internship Employability Using Ensemble Machine Learning Approach | 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