[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118378-en":3,"doc-seo-118378-105":30,"detail-sidebar-cat-0-en-105":95},{"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":4,"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},118378,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Student Acceptance and Satisfaction with Machine Learning Applications in Higher Education Institutions","The study identifies factors driving students’ acceptance and satisfaction with machine learning (ML) usage and adoption in higher education institutions. A descriptive quantitative design used a snowball-sampled survey of 176 students from multiple HEIs. Chi-square tests and regression analysis evaluated relationships among demographic profiles, specialization, ML acceptance, and satisfaction. Results show very high acceptance and high satisfaction with ease of use, while key demographic and specialization variables show no significant links; socioeconomic factors affect satisfaction and adoption.","Student Acceptance and Satisfaction with Machine Learning Applications in Higher Education Institutions  \nAnna Sheila Ilumin Crisostomo1  \n1 Faculty, Oman Tourism College, Muscat, Sultanate of Oman [Email:](Email:1anna.crisostomo@otc.edu.om)[1](Email:1anna.crisostomo@otc.edu.om)[anna.crisostomo@otc.edu.om](Email:1anna.crisostomo@otc.edu.om)  \nCitation: Crisostomo A.S.I., (2024) . Student Acceptance and Satisfaction with Machine Learning Application in Higher Education Institutions. International Journal of Research in Entrepreneurship & Business Studies, 5(4), 15-26.  \n[https://doi.org/10.47259/ijrebs.542](https://doi.org/10.47259/ijrebs.542)  \n[Received on](Received on 27th Jul. 2024)[ 27](Received on 27th Jul. 2024)[th](Received on 27th Jul. 2024)[ Jul. 2024](Received on 27th Jul. 2024)[ ](Received on 27th Jul. 2024)[Revised on](Revised on 19th Sep. 2024)[ 19](Revised on 19th Sep. 2024)[th](Revised on 19th Sep. 2024)[ Sep. 2024](Revised on 19th Sep. 2024)[ ](Revised on 19th Sep. 2024)[Published on](Published on 11th Oct. 2024)[ 11](Published on 11th Oct. 2024)[th](Published on 11th Oct. 2024)[ Oct. 2024](Published on 11th Oct. 2024)  \nCopyright: © 2024 by the authors.  \nLicensee: Global Scientific  \nPublications, Oman.  \nPublishers Note:  \nThis work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This is an openaccess journal and the articles published in this journal are distributed under the terms of CC-BY-SA.  \nAbstract  \nPurpose: The purpose of the study was to identify the factors that influence students’ acceptance and satisfaction of machine learning (ML) usage and adoption; to analyse the acceptance rating and satisfaction of the students in higher education institutions on machine learning applications on their educational experiences and to determine the predictors that influence the acceptance and satisfaction of machine learning techniques among students in higher education institutions.  \nDesign/methodology/approach: This study adopted a descriptive research design and a quantitative approach. Primary data was obtained through a survey questionnaire where snowball sampling was employed with a total of 176 students from different HEIs. Chi-square test and regression analysis were employed to assess the association and relationship between students’demographic profiles, specialization, ML acceptance, and satisfaction. Findings: The results of the study revealed a very high acceptance rating of machine learning among the students and a high level of satisfaction with the ease of use of machine learning techniques. It was also found that there was no significant association between the classification of major/specialization of the field of study and with Acceptance rating of ML and no significant association between the classification of major/specialization and the Ease of use of Machine Learning techniques. It was also found that there was no significant association between Gender with the Satisfaction Rating of MLand there was no significant association between the Gender and the Acceptance Rating of ML. There was no impact of the demographic factors viz. Gender, Nationality, Residence, Age, Classification of Major/Specialization of the study, and the familiarity level on the students’Satisfaction with ML techniques while the socioeconomic factors – Marital Status and the Acceptance Rating of Machine Learning techniques had an impact on the Satisfaction of Machine Learning utilization and adoption in higher education institutions.  \nResearch limitations/implications: The study was focused on students’acceptance and satisfaction with ML utilization and adoption in higher education institutions. This initiative is limited by its sample size and the geographical focus on specific universities.  \nSocial Implications: This study will help policymakers to develop ML applications with intuitive interfaces to ensure accessibility for students to improve user experience and to ensure","cbCaisax9WBrMWn1","https://ap.wps.com/l/cbCaisax9WBrMWn1","pdf",450289,1,12,"English","en",105,"# Abstract\n## Purpose\n## Design/methodology/approach\n## Findings\n## Research limitations/implications\n## Social implications\n## Originality/value\n# Introduction","[{\"question\":\"What was the purpose of the study?\",\"answer\":\"To identify factors influencing students’ acceptance and satisfaction with machine learning usage and adoption in higher education, and to determine predictors affecting these outcomes.\"},{\"question\":\"How was data collected and analyzed?\",\"answer\":\"The study used a descriptive quantitative design with a survey questionnaire and snowball sampling of 176 students. Chi-square tests and regression analysis assessed associations and relationships among demographic factors, specialization, ML acceptance, and satisfaction.\"},{\"question\":\"What did the study find about acceptance and satisfaction levels?\",\"answer\":\"Students showed a very high acceptance rating of machine learning and a high level of satisfaction, particularly regarding the ease of use of ML techniques.\"},{\"question\":\"Which factors influenced satisfaction with ML utilization and adoption?\",\"answer\":\"The study found no significant impact from demographic factors such as gender, nationality, residence, age, specialization, or familiarity level; socioeconomic factors—marital status and ML acceptance—were associated with satisfaction with ML utilization and adoption.\"}]","Student Acceptance and Satisfaction with Machine Learning Applications in Higher Education Institutions | PDF",1785683344,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"student-acceptance-and-satisfaction-with-machine-learning-applications-in-higher-education-institutions","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/student-acceptance-and-satisfaction-with-machine-learning-applications-in-higher-education-institutions/118378/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What was the purpose of the study?","Question",{"text":75,"@type":76},"To identify factors influencing students’ acceptance and satisfaction with machine learning usage and adoption in higher education, and to determine predictors affecting these outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was data collected and analyzed?",{"text":80,"@type":76},"The study used a descriptive quantitative design with a survey questionnaire and snowball sampling of 176 students. Chi-square tests and regression analysis assessed associations and relationships among demographic factors, specialization, ML acceptance, and satisfaction.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the study find about acceptance and satisfaction levels?",{"text":84,"@type":76},"Students showed a very high acceptance rating of machine learning and a high level of satisfaction, particularly regarding the ease of use of ML techniques.",{"name":86,"@type":73,"acceptedAnswer":87},"Which factors influenced satisfaction with ML utilization and adoption?",{"text":88,"@type":76},"The study found no significant impact from demographic factors such as gender, nationality, residence, age, specialization, or familiarity level; socioeconomic factors—marital status and ML acceptance—were associated with satisfaction with ML utilization and adoption.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,126,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":125},"research-report",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},9,"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]