Research on Generalizability of Student Grade Predictive Model in Blended Courses
Collecting data on the student learning process in blended courses and constructing pre-dictive models of learning outcomes can assist instructors in dynamically adjusting instructional strate-gies.A significant obstacle preventing the practical application of predictive studies in blended course settings is the difficulty in generalizing models across different contexts.This paper examines factors that influence the generalizability of grade prediction models in blended courses,including the accuracy of the predictive model,characteristics of the training samples,data processing methods of machine learning algorithms,and the prerequisites for generalization in specific contexts.Using data from all blended courses over two semesters at University A,a grade prediction model was developed and subse-quently applied to case courses at University B.After three semesters of continuous observation,the findings indicate that(1)models trained with larger sample sizes,more complete feature sets,and greater data variability demonstrated enhanced generalizability."High Active"blended courses exhibi-ted these characteristics;(2)both incremental and batch learning methods could construct grade predic-tion models with high accuracy(over 70％),with incremental learning methods proving more effective in generalizing these models;(3)models built using incremental learning data processing algorithms a-chieved higher predictive accuracy when generalized.Furthermore,predictive accuracy was higher when the distribution of student online behavioral data in the test courses resembled that of"High Active"blended courses.These findings provide an empirical foundation and a methodological approach for the generalization of grade prediction models in blended course environments.
student performance predictionblended coursepredictive model generalizationma-chine learningonline learning behavior
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