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.
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Acknowledgment
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).
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Chen, X., Li, Z., Zou, D., Wang, F.L., Xie, H., Wong, L.P. (2024). Leveraging Deep Learning for Classifying Learner-Generated Course Evaluation Texts. In: Ma, W.W.K., Li, C., Fan, C.W., U, L.H., Lu, A. (eds) Blended Learning. Intelligent Computing in Education. ICBL 2024. Lecture Notes in Computer Science, vol 14797. Springer, Singapore. https://doi.org/10.1007/978-981-97-4442-8_24
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