Deep neural networks for the automatic understanding of the semantic content of online course reviews

Xieling CHEN, Di ZOU*, Gary CHENG, Haoran XIE

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review


The rise of massive open online courses (MOOCs) brings rich opportunities for understanding learners' experiences based on analyzing learner-generated content such as course reviews. Traditionally, the unstructured textual data is analyzed qualitatively via manual coding, thus failing to offer a timely understanding of the learner’s experiences. To address this problem, this study explores the ability of deep neural networks (DNNs) to classify the semantic content of course review data automatically. Based on 102,184 reviews from 401 MOOCs collected from the Class Central, the present study developed DNN-empowered models to automatically distinguish a group of semantic categories. Results showed that DNNs, especially recurrent convolutional neural networks (RCNNs), achieve acceptable performance in capturing and learning features of course review texts for understanding their semantic meanings. By dramatically lightening the coding workload and enhancing analysis efficiency, the RCNN classifier proposed in this study allows timely feedback about learners’ experiences, based on which course providers and designers can develop suitable interventions to promote MOOC instructional design.
Original languageEnglish
JournalEducation and Information Technologies
Publication statusE-pub ahead of print - 29 Jun 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.


  • Automatic classification
  • Course reviews
  • Deep neural networks
  • Semantic content
  • xMOOCs


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