Debelu, Endalkachew AbebeGoshu, Ayele Taye2025-11-272025-11-272025Debelu, Endalkachew Abebe. Multilevel regression analysis of learning distractions among higher education students / Endalkachew Abebe Debelu, Ayele Taye Goshu // Advanced education. – 2025. – Iss. 26. – P. 122-141.– Bibliogr.: 56 ref.https://ela.kpi.ua/handle/123456789/77419Learning distractions, both internal and external, disrupt students’ focus and academic performance. Understanding these distractions is crucial for developing effective strategies to mitigate their effects and enhance learning outcomes for university students. Little literature is available on assessing students' learning distractions in Ethiopian universities. The current study aims to identify learning distractions of university students and their correlates using a multilevel regression analysis. A cross-sectional survey with a self-administered questionnaire, quantitative approach grounded in postpositivist and connectivism philosophies, is used to collect data from a sample of 1,380 students at the Addis Ababa Science and Technology University. The data are analyzed using SPSS version 27.0, Winsteps version 5.7.1.0, and R version 4.3.2. The multilevel model with random intercept and random-fixed slopes is used to analyze the learning distraction score of the students. The results show that the average learning distractions score is 33.70 out of 100, indicating moderate learning distraction levels among the students. Most (79.20%) students are in a good state, and 5.07% are academically resilient. About 15.44% of the students need help facing academic problems, and 0.29% require urgent university assistance due to early burnout. A significant portion (51.86%) of the variation in distraction score is due to differences between students’ years of stay at the university. Factors affecting learning distraction score include gender, age, income, time spent on social media, and academic resilience. Female students experience lower learning distraction as compared to males. Learning distraction score decreases with an increase in age. An increase in monthly income increases their learning distraction. Student learning distraction increases as the time spent on social media daily increases. Higher academic resilience reduces their distraction score. Interventions based on behavioral, cognitive, and social cognitive learning theories and self-regulated strategies are crucial for managing learning distractions at the university. The intervention may include well-planned training and awareness creation programs for the university students.enAcademic ResilienceLearning DistractionsLearning OutcomesMultilevel ModelsUniversity Studentsакадемічна стійкістьвідволікання під час навчаннярезультати навчаннябагаторівневі моделістуденти університетівArticleP. 122-141https://doi.org/10.20535/2410-8286.3230750000-0002-7207-65520000-0002-5056-0132