Using Learning Analytics to Explore Responses from Student Conversations with Chatbot for Education

Abstract

—Chatbots simulate human conversations through computer programs using natural language. They are developed for a variety of reasons and purposes, as virtual characters and animators or as an interactive game component. Today, Chatbots for education are widely used to allow students to engage with learning content on an ongoing basis. In this study, students use the Chatbot to query web programming learning content such as code description, coding and problem-solving. However, the Chatbot's successful responses to students' questions are unknown. The teachers also do not know the desired learning content of the students using the Chatbot. Thus, the objectives of this study are to explore the likelihood of a student getting successful responses in each conversation and to identify the learning content desired by the students. The learning analytics method is used to analyze the learning data from the Chatbot. The data analysis performed is descriptive analysis and binomial probability test. The results of the studies showed that the value of successful responses of the Chatbot is high. The learning content most desired by the students is related to three categories of web programming content, namely hypertext preprocessor (PHP), database and structured query language (SQL) and hypertext markup language (HTML). The Chatbot is updated based on the proposed actions to provide more efficient responses.