Dropout Prediction Model for College Students in MOOCs Based on Weighted Multi-feature and SVM

Abstract

Due to the COVID -19 pandemic, MOOCs have become a popular form of learning for college students. However, unlike traditional face-to-face courses, MOOCs offer little faculty supervision, which may result in students being insufficiently motivated to continue learning, ultimately leading to a high dropout rate. Consequently, the problem of high dropout rates in MOOCs requires urgent attention in MOOC research. Predicting dropout rates is the first step to address this problem, and MOOCs have a large amount of behavioral data that can be used for such predictions. Most existing models for predicting MOOC dropout based on behavioral data assign equal weights to each behavioral characteristic, despite the fact that each behavioral characteristic has a different effect on predicting dropout. To address this problem, this paper proposes a dropout prediction model based on the fusion of behavioral data and Support Vector Machine (SVM). This innovative model assigns different weights to different behavior features based on Pearson principle and integrates them as data inputs to the model. Dropout prediction is essentially a binary problem, Support Vector Machine Classifier is then trained using the training dataset 1 and dataset 2. Experimental results on both datasets show that this predictive model outperforms previous models that assign the same weights to the behavior features.