Xiao-Shih: The Educational Intelligent Question Answering Bot on Chinese-Based MOOCs

Hsu, Hao-Hsuan; Huang, Nen-Fu

doi:10.1109/icmla.2018.00213

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…This theme refers to intelligent tutoring systems used in online education. There are 12 studies found in this category, and most of them are proposed intelligent tutors; experiments were conducted to evaluate the tutors [34][35][36][37][38][39][40][41][42][43][44][45].…”

Section: Intelligent Tutorsmentioning

confidence: 99%

“…The results indicated that this approach increases student learning with high scores, while using less time for training compared to textbook-based training. Hsu et al [42] proposed an intelligent question-answering bot entitled Xiao-Shih and improved its precision by using ML. The experimental results showed that the chatbot had a 0.833 precision of correct rate with a 0.044 response rate.…”

Section: Automationmentioning

confidence: 99%

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Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision

2022

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The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature’s themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms.

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Section: Intelligent Tutorsmentioning

confidence: 99%

Section: Automationmentioning

confidence: 99%

Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision

2022

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“…In a recent study, Hsu et Huang [23] developed an educational intelligent question answering bot named Xiao-Shih to solve learner's questions immediately in seconds in a paid course named "Python for data science" on ShareCourse. Machine learning techniques such as NLP and random forest were used.…”

Section: Discussion Facilitatormentioning

confidence: 99%

Teachers’ Roles in Online Environments: How AI Based Techniques Can Ease the Shift Challenges from Face-to-Face to Distance Learning

Marrhich¹,

Lafram²,

Berbiche³

et al. 2021

Int. J. Emerg. Technol. Learn.

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The Covid-19 emergency has brought a mandatory shift to online systems in the education sector worldwide. This document gives an overview about the online teaching challenges encountered from the teachers’ point view, restitutes how the teacher’s role in online settings can be determining in the successfulness of the learning experience and more importantly provides insights into Artificial Intelli-gence techniques that can solve the equation of transferring the role of teachers in face-to-face settings to distance learning environments.

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“…The use of chatbots as educational assistants shows promise, particularly in scenarios demanding scalability, such as Massive Open Online Courses (MOOC). Agents have been demonstrated to improve the customization of individualized assignments [3], advance the learning process through voice-based peer to peer (P2P) assessments [27], and provide meaningful question answering capability [19]. All of these assistant-based chatbot solutions free up the time of teaching assistants and instructors, which is an essential prerequisite to the fundamental MOOC objective of learning at scale.…”

Section: Related Workmentioning

confidence: 99%

“…The version presented in this experiment can not only answer questions in different styles but is also able to provide content summaries and links. The solution is tested by presenting participants with a lesson containing information related to coronavirus disease 2019 (COVID- 19), followed by engagement with the chatbot system, a subsequent evaluation of its responses, and a quiz to quantify its pedagogical efficacy. COVID-19 was chosen as the knowledge base due to its current relevance in society.…”

mentioning

confidence: 99%

Toward an eclectic and malleable multiagent educational assistant

Briel

2021

Comput Appl Eng Educ

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Conversational agents are systems capable of processing and responding to human language. They have evolved over the years from a means to pass the Turing Test to chatbots that fulfill a utilitarian purpose. Closed-domain chatbots are specialized in a specific knowledge base and are often used in an attempt to assist users in an educational context. Existing open-source, educational assistant chatbots are narrow in immediate functionality, thus lim-

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Xiao-Shih: The Educational Intelligent Question Answering Bot on Chinese-Based MOOCs

Cited by 7 publications

References 7 publications

Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision

Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision

Teachers’ Roles in Online Environments: How AI Based Techniques Can Ease the Shift Challenges from Face-to-Face to Distance Learning

Toward an eclectic and malleable multiagent educational assistant

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