[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127260-en":3,"doc-seo-127260-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127260,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","An Automated Machine Learning Approach for Early Identification of At-Risk Maritime Students - Research Report","Machine Learning (ML) offers major potential for improving education, especially Maritime Education and Training (MET), where ML benefits are not yet fully realized. This study explores ML approaches to predict future performance and to identify at-risk maritime students at the beginning of their degree programs. Early identification supports more efficient planning and execution of instructional strategies by surfacing learning gaps for students and teachers. The work addresses the limited evidence of ML methods in maritime-specific learning contexts.","An Automated Machine Learning Approach for Early Identification of At-Risk Maritime Students  \nHasan Mahbub Tusher 1 , Ziaul Haque Munim 1 , Sajid Hussain2 , and Salman Nazir 1  \n1 Department of Maritime Operations, University of South-Eastern Norway, 3184, Horten, Norway  \n2 Bangladesh Marine Academy, Chattogram 4206, Bangladesh  \nABSTRACT  \nMachine Learning (ML) presents a significant opportunity for the field of education, including Maritime Education and Training (MET) . The benefits of ML have yet to be fully realized within MET. By utilizing ML-powered methods into maritime education, institutions can better prepare future seafarers while providing accurate, state-of-the-art education tailored to individual student needs. Early identification of areas for improvement can help students and teachers enhance educational outcomes within MET. This study presents the potential of ML approaches for predicting future performance as well as for identifying at-risk maritime students at the initial stages of their degree program. By enabling early identification, institutions can more efficiently plan and execute instructional strategies.  \nKeywords: Machine learning, Performance prediction, Maritime education, Learning analytics  \nINTRODUCTION  \nMachine Learning (ML) and Artificial Intelligence (AI) have facilitated databacked decision-support system for educators across all domains (Chen et al., 2020) . It involves training models on data to make predictions or decisions without the need for human intervention. Nowadays ML methods are being used for objective analysis of learners' performance (Alkadri et al., 2021), providing adaptive learning content to the students (Edwards et al., 2018), even psychological evaluation of learners (Liu et al., 2020) in a variety of contexts.  \nA novel application of ML and AI in education includes identifying students who may need extended supervision or are at-risk of dropout, i.e., identification of at-risk students during the early stages of their studies (Chui et al., 2020; Waheed et al., 2020; Xing & Du, 2019) . ML algorithms are a perfect fit for this purpose especially due to their inherent capacity to identify patterns and trends in students' data along with making predictions based on the data (Chen et al., 2020) . These characteristics enable ML algorithms tobe used for monitoring students' development and analysing the present and  \n© 2023 . Published by AHFE Open Access. All rights reserved. 47  \npredicted performances in a specific learning context. However, there is a lack of literature employing these methods in the maritime education context.  \nMaritime education, or more specifically seafaring education is generally divided into two separate streams, i.e., engineering and navigation. Maritime Education and Training (MET) providers offer educational components to prospective seafarers satisfying the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW, 2011) . Depending on the country or administration, universities may offer maritime degrees (e.g., Bachelor's in Marine Engineering or Bachelor's in Nautical Science etc.) that include relevant course components along with on-the-job training at sea and a few post-sea study components (IAMU, 2023) . With the increasing demand for quality education as well as for workplace-relevance of seafarers' training, it has become crucial to understand which factors affect the learning trajectory of seafarers during the early stages of their degree education.  \nThis study demonstrates an application of ML enabling early prediction of specific courses that instructors at maritime institutes may use to identify areas where their students need improvement. By providing instructors with targeted insights into students' strengths and weaknesses, this system can help them tailor their teaching approaches and offer additional support where necessary. Ultimately, this can help maritime degree students to succe","cbCaietxjszZ9HEK","https://ap.wps.com/l/cbCaietxjszZ9HEK","pdf",1070626,2,1,9,"English","en",105,"# Introduction\n## Machine Learning in education and performance analysis\n## At-risk identification in early study stages\n# Related Studies\n## ML applications across educational contexts\n## Type of variables and methods used\n## Automated Machine Learning (AutoML) for model building","[{\"question\":\"How can machine learning support maritime education and training (MET)?\",\"answer\":\"The document argues that ML can enable objective analysis of learners, support adaptive learning, and improve educational outcomes in MET by identifying learning gaps early.\"},{\"question\":\"What is the main goal of the study in terms of student outcomes?\",\"answer\":\"It aims to use ML to predict future performance and identify at-risk maritime students at early stages so institutions can plan targeted instructional strategies.\"},{\"question\":\"Why is automated machine learning (AutoML) relevant in this work?\",\"answer\":\"AutoML automates key steps of building ML models, such as data pre-processing, feature engineering, model selection, and tuning, reducing the need for extensive manual effort.\"}]","An Automated Machine Learning Approach for Early Identification of At-Risk Maritime Students - 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