Statistical evaluation of the impact of microlearning and adaptive teaching algorithms on student performance
Year: 2025
Type: article
Study design: Clinical Trial
Abstract: This study examines the impact of combining microlearning, virtual laboratories, and adaptive teaching algorithms on student motivation, engagement, and academic performance in an open and distance learning (ODL) setting. An experimental design involving 61 university students was used, with participants divided into experimental and control groups. The instructional module integrated short-form content, interactive virtual lab tasks, and adaptive strategies. Data were collected via structured questionnaires and academic records from the EMPRO system. Using SPSS, descriptive statistics, T-tests, N-Gain, and ANCOVA were applied. Results showed significant improvements in motivation and academic achievement in the experimental group (p < 0.05), while engagement levels remained comparably high across both groups. N-Gain analysis revealed a strong learning gain (0.529) in the experimental group versus 0.112 in the control group. ANCOVA confirmed that the learning gains were primarily due to the intervention itself. Overall, the study supports the effectiveness of a microlearning-based adaptive model in enhancing student outcomes in ODL environments.
Source: Informatics and Control Problems
Author: T. Osmanli
Institution: National Aviation Academy
Language: English
Topic: E-Learning and COVID-19
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W7117649379