The System Architecture of Conversational Intelligent Tutorin ...

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  • Title: The System Architecture of Conversational Intelligent Tutoring Systems (CITS) Using Latent Semantic Analysis
  • Author(s): Kanokrat Jirasajanukul, Panita Wannapiroon
  • Publisher: Common Ground Research Networks
  • Collection: Common Ground Research Networks
  • Series: The Learner
  • Journal Title: The International Journal of Technologies in Learning
  • Keywords: Conversational Intelligent Tutoring Systems, Latent Semantic Analysis
  • Volume: 26
  • Issue: 1
  • Year: 2019
  • ISSN: 2327-0144 (Print)
  • ISSN: 2327-2686 (Online)
  • DOI: https://doi.org/10.18848/2327-0144/CGP/v26i01/49-56
  • Citation: Jirasajanukul, Kanokrat , and Panita Wannapiroon. 2019. "The System Architecture of Conversational Intelligent Tutoring Systems (CITS) Using Latent Semantic Analysis." The International Journal of Technologies in Learning 26 (1): 49-56. doi:10.18848/2327-0144/CGP/v26i01/49-56.
  • Extent: 8 pages

Abstract

The objective of this research is to design and evaluate the system architecture of Conversational Intelligent Tutoring Systems (CITS) using latent semantic analysis (LSA). The methodology that is used can be divided into two phases: 1) the design of the system architecture of Conversational Intelligent Tutoring Systems using latent semantic analysis; and 2) the evaluation of the suitability of the system architecture of Conversational Intelligent Tutoring Systems using latent semantic analysis. The sample group includes twelve experts with at least five years of experience from the fields of CITS, LSA, and education, all of whom were asked to participate using purposive sampling. The research tools that are used in this research consist of system architecture and evaluation forms to certify the suitability of the system. The statistics employed in this research include arithmetic mean and standard deviation. The results show that the overall architecture has the highest level of suitability (mean = 4.71, S.D. = 0.451). The research results indicate that the system architecture of Conversational Intelligent Tutoring Systems using latent semantic analysis can be applied to develop real systems that promote and support self-learning. In addition, the system can satisfy the needs of learning, accommodate problem-solving, and offer answers to the learners in an intelligent manner.