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In order to improve students’ learning performance, early and accurately identify at-risk students, so that teachers can early intervention, is the focus topic of many related research.
Blended course is a course which combine online and offline learning, different from traditional offline learning, students are also able to learn through the online learning platform. However, students will leave a lot of records in the learning process, such as students′ homework grade, video viewing behavior, online activity frequency, online test grade etc. Therefore, this paper based on data mining and machine learning technologies, collects students’ learning activity data from a blended calculus course, uses multiple linear regression to predict students’ final grade.
Related researchs point out the accuracy of the prediction model is easily affected by outliers. Therefore, this paper uses RANSAC algorithm as outlier detection method to remove outliers from data. In order to futher improve accuracy of prediction model after remove outliers, this paper uses T-Test as feature selection method, retains the key features that have a significant impact on the final grade, to futher improve accuracy of prediction model.
According to the results of research, through the outlier detection and feature selection process proposed in this paper, prediction error from 15.516 down to 4.571 points, improving the prediction error about 70 percent. | en_US |