000162373 001__ 162373
000162373 005__ 20251017144650.0
000162373 0247_ $$2doi$$a10.1080/2331186X.2025.2529420
000162373 0248_ $$2sideral$$a144927
000162373 037__ $$aART-2025-144927
000162373 041__ $$aeng
000162373 100__ $$0(orcid)0000-0002-9140-9367$$aVal, Sonia$$uUniversidad de Zaragoza
000162373 245__ $$aKey performance indicators for optimizing academic performance and course design in online educational platforms
000162373 260__ $$c2025
000162373 5060_ $$aAccess copy available to the general public$$fUnrestricted
000162373 5203_ $$aThis study investigates how Key Performance Indicators (hereafter KPI) can enhance academic performance and inform course design on online educational platforms. As part of the AI4Ed project (Erasmus+), it bridges educational theory with computational analytics to develop data-driven, adaptive e-learning systems. This study employed a quantitative, correlational design to examine the relationship between students’ interaction patterns on a digital learning platform and their academic performance, and data from 63 postgraduate students were analyzed through Moodle interaction logs. High-frequency tasks, forum contributions, and resource downloads were evaluated as predictors of performance. The results highlight the predictive strength of active engagement and suggest practical strategies for individualized support, with activity peaks near task deadlines and gradual declines indicative of academic fatigue. The findings emphasize the importance of adaptive engagement strategies and demonstrate how KPI can support personalized learning and real-time course adjustments, enhancing online learning environments and fostering self-regulated learning. These findings have practical implications for educators and institutions aiming to enhance adaptive learning systems, optimize student engagement, and design data-informed interventions in online education. The study contributes to the growing field of learning analytics by proposing a KPI-driven framework grounded in cognitive and didactic theory.
000162373 536__ $$9info:eu-repo/grantAgreement/ES/DGA/S57-23R
000162373 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000162373 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000162373 700__ $$0(orcid)0000-0003-4621-6993$$aQuintas, Alejandro$$uUniversidad de Zaragoza
000162373 7102_ $$14001$$2215$$aUniversidad de Zaragoza$$bDpto. Ciencias de la Educación$$cÁrea Didáctica y Organiz. Esc.
000162373 7102_ $$15002$$2515$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Ing. Procesos Fabricación
000162373 773__ $$g12, 1 (2025), [25 pp.]$$tCogent Education$$x2331-186X
000162373 8564_ $$s3565098$$uhttps://zaguan.unizar.es/record/162373/files/texto_completo.pdf$$yVersión publicada
000162373 8564_ $$s997929$$uhttps://zaguan.unizar.es/record/162373/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000162373 909CO $$ooai:zaguan.unizar.es:162373$$particulos$$pdriver
000162373 951__ $$a2025-10-17-14:35:58
000162373 980__ $$aARTICLE