Analysis of the Influence of Assessment Time on Grading Weight in Online Learning
DOI:
https://doi.org/10.59683/ijle.v2i1.323Keywords:
Online Learning Assessment, Grading Weight, Simple Linear Regression, Learning Analytics, OULAD.Abstract
This study aims to analyze the influence of assessment time on grading weight in online learning using a public dataset from the Open University Learning Analytics Dataset (OULAD) via Kaggle. A quantitative approach with correlational research design was applied, utilizing simple linear regression as the analytical method processed with IBM SPSS Statistics. The independent variable in this study is assessment execution time (date), while the dependent variable is grading weight (weight). The results of the ANOVA test show a significance value of 0.008 (< 0.05), indicating that the regression model is statistically significant. Meanwhile, the correlation coefficient (R) of 0.189 indicates a weak positive relationship, and the coefficient of determination (R Square) of 0.036 reveals that assessment time only explains 3.6% of the variance in grading weight. These findings confirm that assessment time has a statistically significant but practically weak influence on grading weight. Other variables, such as assessment type, material complexity, and institutional policies, are likely to have a larger contribution. This study provides theoretical and practical implications for the design of digital instructional systems, especially in the context of facilitating learning and improving human performance through structured assessment management.
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