{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T16:03:30Z","timestamp":1765382610300,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T00:00:00Z","timestamp":1742688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Computer-driven assessment has revolutionized the way educational and professional assessments are conducted. Using artificial intelligence for data analytics, computer-based assessment improves efficiency, accuracy, and optimization of learning across disciplines. Optimizing e-learning requires a structured approach to analyzing learners\u2019 progress and adjusting instruction accordingly. Although learning effectiveness is influenced by numerous parameters, competency-based assessment provides a structured and measurable way to evaluate learners\u2019 achievements. This study explores the application of artificial intelligence algorithms to optimize e-learners\u2019 studying within a generalized e-course framework. A competency-based assessment model was developed using weighted parameters derived from Bloom\u2019s taxonomy. The key contribution of this work is an innovative method for calculating competency scores using weighted attributes and a dynamic assessment parameter, making the optimization process applicable to both learners and instructors. The results indicate that using the weighted attribute method with a dynamic assessment parameter can improve the structuring of e-courses, increase learner engagement, and provide instructors with a clearer understanding of learners\u2019 progress. The proposed approach supports data-driven decision making in e-learning, ensuring a personalized learning experience, and improving overall learning outcomes.<\/jats:p>","DOI":"10.3390\/computers14040116","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T04:48:18Z","timestamp":1742791698000},"page":"116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Computer-Driven Assessment of Weighted Attributes for E-Learning Optimization"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2980-3678","authenticated-orcid":false,"given":"Olga","family":"Ovt\u0161arenko","sequence":"first","affiliation":[{"name":"Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, Saul\u0117tekio al. 11, LT-10223 Vilnius, Lithuania"},{"name":"Centre for Sciences, TTK University of Applied Sciences, P\u00e4rnu mnt. 62, 10135 Tallinn, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2868-5796","authenticated-orcid":false,"given":"Elena","family":"Safiulina","sequence":"additional","affiliation":[{"name":"Centre for Sciences, TTK University of Applied Sciences, P\u00e4rnu mnt. 62, 10135 Tallinn, Estonia"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s44217-024-00313-5","article-title":"Opportunities of Machine Learning Algorithms for Education","volume":"3","year":"2024","journal-title":"Discov. 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