Classification of Discussion Threads in MOOC Forums Based on Deep Learning
Year: 2018
Type: article
Abstract: As the only way for students and instructors to communicate in Massive Open Online Course (MOOCs), MOOC forum plays an important role in supervision of students’ learning. To guarantee the utilization of MOOC forums, classification of discussion threads is essential and should be reasonable. In this paper, we propose a model constructed by the convolutional neural network in deep learning to classify discussion threads in MOOC forums. In order to ensure that our model is not restricted by course contents and teaching languages, we define features (exactly 18) based on user interactive behaviors rather than the contents of threads. To validate our model and features, two groups of contrast experiments are conducted on Rossi’s dataset. Experimental results show that our features are superior to Rossi’s in measuring the characteristics of threads in different subforums. Meanwhile, the performance of deep model proposed in this paper is also much better than Rossi’s model and other models constructed by traditional classification algorithms. Especially the performance of our model with our features is increased by 21% on average than Rossi’ work. For Meetups and Assignments, the AUC are up to 0.919 and 0.882 respectively.
Authors: Lin FENG, Lei WANG, Sheng-lan LIU, Guo-chao LIU
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W2792000464