Leveraging Deep Learning for Classifying Learner-Generated Course Evaluation Texts

Xieling CHEN, Zongxi LI, Di ZOU, Fu Lee WANG*, Haoran XIE, Leung Pun WONG

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterReferred Conference Paperpeer-review

Abstract

With the growing popularity of massive open online courses (MOOCs), there are many chances to analyze student-generated assessments of course content to learn more about the experiences of learners. Unstructured textual data is typically subjected to manual analysis for qualitative evaluation, which produces a limited knowledge of learners’ experiences. This study looked into the use of a convolutional neural networks-bidirectional long short-term memory network (CNN-BiLSTM) hybrid neural network model to automatically classify significant content in student-written course evaluation documents. Nine categories: “Platforms and tools”, “Overall evaluation”, “Course introduction”, “Course quality”, “Learning resources”, “Instructor”, “Learner”, “Relationship”, “Process”, and “Assessment” were successfully recognized by the artificial neural network-based model. Using a well-established coding framework, an annotated dataset of learner-generated course evaluations was built, and 8,588 MOOC review words from Class Central were analyzed to assess the model’s performance. The CNN-BiLSTM model performed better than the other models when compared to the baseline techniques. It obtained an overall F1 score of 0.7563, an accuracy score of 0.807, a recall score of 0.7552, and a precision score of 0.7612. The CNN-BiLSTM model quickly and efficiently extracts semantic information from contexts by comprehending the local and global features of learner-generated course evaluation texts. With this knowledge, learner-generated course assessments can be managed more effectively, which could improve communication between teachers and students.
Original languageEnglish
Title of host publicationBlended Learning : Intelligent Computing in Education
EditorsWill W. K. MA, Chen LI, Chun Wai FAN, Leong Hou U, Angel LU
PublisherSpringer Singapore
Chapter24
Pages311-321
ISBN (Electronic)9789819744428
ISBN (Print)9789819744411
DOIs
Publication statusPublished - 29 Jun 2024
Event17th International Conference on Blended Learning, ICBL 2024 - University of Macau, Macao, Macao
Duration: 29 Jul 20241 Aug 2024
https://hksmic.org.hk/icbl/2024/

Publication series

NameLecture Notes in Computer Science
Volume14797
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Blended Learning, ICBL 2024
Abbreviated titleICBL 2024
Country/TerritoryMacao
CityMacao
Period29/07/241/08/24
Internet address

Funding

This work was supported by the National Natural Science Foundation of China (No. 62307010). Zongxi Li’s work was done in HKMU and was partially supported by Hong Kong Metropolitan University Research Grant (No. RD/2022/1.14).

Fingerprint

Dive into the research topics of 'Leveraging Deep Learning for Classifying Learner-Generated Course Evaluation Texts'. Together they form a unique fingerprint.

Cite this