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Advantages andConstraints of aHybrid Model K-12E-Learning AssistantChatbot Eric Hsiao‐Kuang WuChun-Han LinYu‐Yen Ou2020 год

Advantages and Constraints of a Hybrid Model K-12 E-Learning Assistant Chatbot
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Аннотация: E-Learning has become more and more popular in recent years with the advance of new technologies. Using their mobile devices, people can expand their knowledge anytime and anywhere. E-Learning also makes it possible for people to manage their learning progression freely and follow their own learning style. However, studies show that E-Learning can cause the user to experience feelings of isolation and detachment due to the lack of human-like interactions in most E-Learning platforms. These feelings could reduce the user's motivation to learn. In this paper, we explore and evaluate how well current chatbot technologies assist users' learning on E-Learning platforms and how these technologies could possibly reduce problems such as feelings of isolation and detachment. For evaluation, we specifically designed a chatbot to be an E-Learning assistant. The NLP core of our chatbot is based on two different models: a retrieval-based model and a QANet model. We designed this two-model hybrid chatbot to be used alongside an E-Learning platform. The core response context of our chatbot is not only designed with course materials in mind but also everyday conversation and chitchat, which make it feel more like a human companion. Experiment and questionnaire evaluation results show that chatbots could be helpful in learning and could potentially reduce E-Learning users' feelings of isolation and detachment. Our chatbot also performed better than the teacher counselling service in the E-Learning platform on which the chatbot is based.
Год издания: 2020
Авторы: Eric Hsiao‐Kuang Wu, Chun-Han Lin, Yu‐Yen Ou, Chen-Zhong Liu, Wei-Kai Wang, Chi-Yun Chao
Издательство: Institute of Electrical and Electronics Engineers
Источник: IEEE Access
Ключевые слова: AI in Service Interactions, Recommender Systems and Techniques, Advanced Text Analysis Techniques
Другие ссылки: IEEE Access (PDF)
IEEE Access (HTML)
DOAJ (DOAJ: Directory of Open Access Journals) (HTML)