[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126311-en":3,"doc-seo-126311-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126311,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Utilizing Machine Learning Techniques to Predict University Students’ Digital Competence","Digital competence (DC) is essential in the technological age for teachers, students, communities, and governments, motivating universities to adopt modern technologies in teaching and learning. The study measures university students’ DC in artificial intelligence (AI) applications and systems using machine learning (ML) techniques. Using a descriptive approach with 400 male and female Baghdad University students across gender and academic specialization, the research applies a questionnaire to assess DC levels. Results show students’ DC was not high and was moderate for AI-related competence, with differences favoring female students in scientific specializations.","JEP  \nInternational Journal of  \nEngineering Pedagogy  \niJEP | eISSN: 2192-4880 | Vol. 15 No. 3 (2025) |  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \n[https://doi.org/10.3991/ijep.v15i3.54943](https://doi.org/10.3991/ijep.v15i3.54943)  \nPAPER  \nUtilizing Machine Learning Techniques to Predict University Students’ Digital Competence  \nWisal Hashim Abdulsalam1, Zainab Hazim Ibrahim1, Ban Hassan Majeed1(*), Haider TH. Salim ALRikabi2  \n1Computer Department, College of Education for Pure Science/Ibn A Haitham, University of Baghdad, Baghdad, Iraq  \n2Electrical Engineering Department, College of Engineering, Wasit University, Wasit, ALKut, Iraq  \nban.h.m@ihcoedu. [uobaghdad.edu.iq](uobaghdad.edu.iq)  \nABSTRACT  \nGiven the importance of possessing the digital competence (DC) required by the technological age, whether for teachers or students and even communities and governments, educational institutions in most countries have sought to benefit from modern technologies brought about by the technological revolution in developing learning and teaching and using modern technologies in providing educational services to learners. Since university students will have the doors to work opened in all fields, the research aims to know their level of DC in artificial intelligence (AI) applications and systems utilizing machine learning (ML) techniques. The descriptive approach was used, as the research community consisted of students from the University of Baghdad in its colleges with scientific and human specializations. To measure the level of DC, a questionnaire was applied as a data collection tool to a sample of 400 male and female students, distributed based on gender and academic specialization. The results showed that the sample students did not have high DC. Their possession of DC related to AI applications and systems was to a moderate degree. The results indicated that there were differences in the responses of the study sample members due to the gender variable and the specialization variable, in favor of the female students with scientific specialization.  \nKEYWORDS  \ndigital competence (DC), artificial intelligence (AI), digital education, university, students, machine learning (ML), support vector classifier (SVC), Gaussian Naïve Bayes (GNB)  \n1 INTRODUCTION  \nUniversities are educational institutions that use digital educational technologies in teaching, learning, and research methods and in all university functions. Whereas modern technology and artificial intelligence (AI) play a major role in improving students’ access to education. AI and technology have affected professors and students alike. Teaching bodies have acquired new teaching skills thanks to modern technologies such as digital tablets, laptops, and computers. As for the students, they have become proficient in using AI applications in their educational programs, which has  \nAbdulsalam, W. H., Ibrahim, Z. H., Majeed, B. H., Salim ALRikabi, H.T. (2025) . Utilizing Machine Learning Techniques to Predict University Students’Digital Competence. International Journal of Engineering Pedagogy (iJEP), 15(3), pp. 75–91. [https://doi.org/10.3991/ijep.v15i3.54943](https://doi.org/10.3991/ijep.v15i3.54943)[ ](https://doi.org/10.3991/ijep.v15i3.54943)[Article submitted 2024-11-16. Revision uploaded 2025-01-28. Final acceptance 2025-02-17.](Article submitted 2024-11-16. Revision uploaded 2025-01-28. Final acceptance 2025-02-17.)  \n© 2025 by the authors of this article. Published under CC-BY.  \niJEP | Vol. 15 No. 3 (2025) International Journal of Engineering Pedagogy (iJEP) 75  \nAbdulsalam et al.  \ncontributed to raising their abilities to deal effectively with this advanced type of technology and pushing them towards a successful life [1] .  \nSmart applications based on AI are expected to be more present and important in imparting education for demands that come in a general or evaluative form; they provide f","cbCaiumDZCidSEoF","https://ap.wps.com/l/cbCaiumDZCidSEoF","pdf",1684164,7,1,17,"English","en",105,"# Abstract\n# Introduction\n## Digital education and AI impact\n## Research significance and gap\n## Applied evaluation and objectives\n# Methods and measurement approach\n## Sample and questionnaire\n# Results and findings","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To determine university students’ level of digital competence (DC) related to AI applications and systems using machine learning (ML) techniques.\"},{\"question\":\"How was students’ digital competence measured?\",\"answer\":\"A questionnaire was used as the data collection tool, applied to a sample of 400 male and female students.\"},{\"question\":\"What did the results indicate about students’ digital competence?\",\"answer\":\"Students did not show a high overall DC; their competence related to AI applications and systems was at a moderate degree, with differences by gender and specialization favoring female students in scientific specializations.\"}]","Utilizing Machine Learning Techniques to Predict University Students’ Digital Competence | PDF",1785904389,43,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"utilizing-machine-learning-techniques-to-predict-university-students-digital-competence","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/utilizing-machine-learning-techniques-to-predict-university-students-digital-competence/126311/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main objective of the study?","Question",{"text":77,"@type":78},"To determine university students’ level of digital competence (DC) related to AI applications and systems using machine learning (ML) techniques.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How was students’ digital competence measured?",{"text":82,"@type":78},"A questionnaire was used as the data collection tool, applied to a sample of 400 male and female students.",{"name":84,"@type":75,"acceptedAnswer":85},"What did the results indicate about students’ digital competence?",{"text":86,"@type":78},"Students did not show a high overall DC; 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