Abstract
In this paper, we present a Natural Language Processing (NLP)-empowered virtual course assistant solution that supports online teaching and learning in the context of the COVID-19 pandemic. We leverage advanced technologies of pre-trained language models in NLP to construct several fundamental functionalities for the virtual course assistant. The assistant is designed to answer general course enquiries to reduce time-consuming and repeated human responses, to answer course-related knowledge questions by understanding both queries and teaching materials, and to analyze students' feedback via sentiment analysis. Additionally, we have constructed the course-related database and cross-platform virtual assistants for both website and mobile applications. Different pre-trained models are utilized to fine-tune the dataset in each type of model. By comparing different datasets and analyzing their performance, the best performance model is selected for the virtual assistant. Empirically, adopting NLP-empowered virtual course assistants in class improves teaching and learning experiences: With the help of an NLP-empowered virtual course assistant, the teaching team could devote more effort and time to answering complex questions; For students, an immediate response increases their motivation to study. Thus, the online system could give an excellent user experience to a wide variety of users. Our code and dataset are released at https://github.com/Heriannan/NLP-for-educationVirtualAssistant. © 2022 IEEE.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2022 |
| Publisher | IEEE |
| Pages | 373-380 |
| ISBN (Electronic) | 9781665491174 |
| ISBN (Print) | 9781665491181 |
| DOIs | |
| Publication status | Published - Dec 2022 |
| Event | 10th IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE 2022): Transforming Educational Technologies and Pedagogies for the Next Decade - Virtual, The Hong Kong Polytechnic University, Hong Kong, China Duration: 4 Dec 2022 → 7 Dec 2022 https://2022.tale-conference.org/tale2022.html |
Publication series
| Name | Proceedings - IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE |
|---|---|
| ISSN (Print) | 2374-0191 |
| ISSN (Electronic) | 2470-6698 |
Conference
| Conference | 10th IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE 2022) |
|---|---|
| Abbreviated title | IEEE TALE 2022 |
| Place | Hong Kong, China |
| Period | 4/12/22 → 7/12/22 |
| Internet address |
Funding
This work was supported in part by the Hong Kong UGC Special Virtual Teaching and Learning (VTL) Grant 6430300, the InnoHK initiative, the Government of the HKSAR, Laboratory for AI-Powered Financial Technologies, and the International Collaborative Research Program of Guangdong Science and Technology Department under Grant No.2020A0505100061.
Research Keywords
- Educational Virtual Assistant
- Natural Language Processing
- Question Answering
- Sentiment Analysis
RGC Funding Information
- RGC-funded
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