{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T19:09:22Z","timestamp":1770836962899,"version":"3.50.1"},"reference-count":34,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:00:00Z","timestamp":1759449600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:00:00Z","timestamp":1759449600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Comp Applic In Engineering"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Machine learning (ML) has the potential to enhance educational predictive analytics, but its adoption is limited by the programming expertise required to develop models. Traditional ML tools require coding skills, which makes them inaccessible to educators and researchers without computational backgrounds. Existing no\u2010code platforms lack affordability and accessibility. This study addresses this gap by developing and validating a no\u2010code ML builder to enable non\u2010programmers to build, evaluate, and deploy ML models. Design and development research approach was adopted in the study. It utilizes Python\u2010based tools such as Streamlit and scikit\u2010learn. The tool underwent expert validation and comparative performance testing against Google Colab using datasets from Kaggle, consisting of 5000 and 2392 student performance records. The results show that the no\u2010code ML builder, which is accessible at nextml.streamlit.app achieved a predictive performance comparable to coded models. A minor performance gap was observed in some algorithms, with Logistic Regression achieving an accuracy of 63.88% compared to 73.28% in Google Colab. Experts in educational technology and computer science rated the tool highly for usability, with mean scores ranging from 4.33 to 4.57. 71% of evaluators found it suitable for educational datasets, and 56% endorsed its ability to handle students' data sets. The study concludes that the tool bridges the accessibility gap in the application of ML in education while maintaining competitive model performance. It recommends that Institutions adopt no\u2010code tools. Future research should focus on incorporating more complex algorithms.<\/jats:p>","DOI":"10.1002\/cae.70088","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T07:31:42Z","timestamp":1759476702000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a No\u2010Code Machine Learning Model Builder for Predictive Analytics in Education"],"prefix":"10.1002","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3597-151X","authenticated-orcid":false,"given":"Mohammed","family":"Jibril","sequence":"first","affiliation":[{"name":"Department of Educational Technology, Faculty of Education University of Ilorin Ilorin Nigeria"}]}],"member":"311","published-online":{"date-parts":[[2025,10,3]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899x\/1051\/1\/012005"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/feduc.2023.1244686"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.60084\/jeml.v1i2.132"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10639-023-11700-0"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2019.103724"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10639-024-13113-z"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-32484-w"},{"issue":"1","key":"e_1_2_9_9_1","first-page":"8","article-title":"Smart Automated Grading System Using Machine Learning Algorithm for Short Answers Questions","volume":"29","author":"Hasan D. 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B.","year":"2021","journal-title":"International Journal of Trend in Scientific Research and Development"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.11591\/ijeecs.v32.i1.pp363-371"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/educsci14040417"},{"issue":"3","key":"e_1_2_9_25_1","first-page":"4132","article-title":"Implementation of Machine Learning Based Google Teachable Machine in Early Childhood Education","volume":"14","author":"Prasad P. Y.","year":"2022","journal-title":"International Journal of Early Childhood Special Education"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-07005-1_14"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iintel.2023.100028"},{"issue":"1","key":"e_1_2_9_28_1","first-page":"1","article-title":"Towards the Revolution and Democratization of Education: A Framework to Overcome Challenges and Explore Opportunities Through Industry 4.0","volume":"21","author":"Costa A. C. F.","year":"2022","journal-title":"Informatics in Education"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/app14188236"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.61487\/jiste.v1i2.13"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.46328\/ijte.285"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.6007\/ijarbss\/v13-i3\/15773"},{"key":"e_1_2_9_33_1","unstructured":"I.Adamo Students Performance in 2024 JAMB (Kaggle.com. 2024) https:\/\/www.kaggle.com\/datasets\/idowuadamo\/students-performance-in-2024-jamb."},{"key":"e_1_2_9_34_1","unstructured":"R.El Kharoua Students Performance Dataset (Kaggle.com. 2024) https:\/\/www.kaggle.com\/datasets\/rabieelkharoua\/students-performance-dataset."},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.3390\/su15065232"}],"container-title":["Computer Applications in Engineering Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cae.70088","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/cae.70088","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cae.70088","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:13:21Z","timestamp":1770833601000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cae.70088"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,3]]},"references-count":34,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1002\/cae.70088"],"URL":"https:\/\/doi.org\/10.1002\/cae.70088","archive":["Portico"],"relation":{},"ISSN":["1061-3773","1099-0542"],"issn-type":[{"value":"1061-3773","type":"print"},{"value":"1099-0542","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,3]]},"assertion":[{"value":"2025-04-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70088"}}