An Exploratory Posts Detecting Method for MOOC Forums Based on Deep Learning postprint

Abstract: [Purpose/significance] Massive Open Online Course (MOOC) forum is an important source to acquire user review data. Automatically detecting exploratory dialogues with high knowledge density from large amounts of unlabeled data and mining its potential value has a significant impact on the improvement of teaching quality and students’ mastery of knowledge. [Method/process] We proposed a new auto-detecting model based on deep learning, which firstly uses GloVe algorithm to train word embedding to reinforce semantic understanding for texts and then adopts Convolutional Neural Network (CNN) to automatically learn text features and make classifications on exploratory dialogues. An empirical and comparative study was done on the annotated dataset from the online course Introduction to Psychology on the platform of Xuetang. [Result/conclusion] Experiment result shows that using the word embedding pretrained by GloVe and fine tune it while training can improve the performance of our model. Our model gets the F1 score of 0.94, which is greatly improved compared with Naive Bayes model (0.88), Logistic Regression model (0.89), Decision Tree model (0.88) and Random Forest model (0.88) and exhibits great practicality with low learning costs.