Tabriz Osmanli
Statistical evaluation of the impact of microlearning and adaptive teaching algorithms on student performance
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.
Keywords: Microlearning, Adaptive teaching algorithms, Virtual laboratory, Student motivation, Academic achievement, Open and distance learning, Educational data analysis
DOI: https://doi.org/10.54381/icp.2025.2.09
Statistical evaluation of the impact of microlearning and adaptive teaching algorithms on student performance
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.
Keywords: Microlearning, Adaptive teaching algorithms, Virtual laboratory, Student motivation, Academic achievement, Open and distance learning, Educational data analysis
DOI: https://doi.org/10.54381/icp.2025.2.09