Data Mining in E-learning Accomplishment by Underachievers with SPSS and WEKA

This study was conducted to investigate the e-learning performance of underachievers in college English and their learning outcomes measured by their CET 4 scores through the statistical means of SPSS and the predictive modeling of J48 Decision Tree with WEKA. The statistics e-learning tasks by underachievers were processed with the statistic software of SPSS for exploring the differences caused by variables of genders and majors alongside with the correlations among different task types. The J48 Decision Tree in WEKA rendered an in-depth insight into the hierarchical nature in e-learning tasks and their effects on the success in the CET 4. In the light of J48 pruned tree, rules could be concluded in the prediction of passing CET 4.