Improving essential knowledge and self-efficacy in computers network course: The potential of chatbots

Abstract

In university-level education, the computer network is a mandatory course for computer science and engineering majors. A computer network course provides theoretical and hands-on foundations for network connectivity, network topologies, internet protocols, networking devices, and gateways. In the traditional classroom, students face challenges in gaining essential knowledge and self-efficacy. Teachers face challenges in assessing students’ quality of knowledge due to the abundance of online computer science teaching resources. Chatbot technologies have the potential to overcome these challenges and enhance essential knowledge and self-efficacy. However, the argument remains whether a course-specific standard chatbot or a generalizable chatbot could produce high-quality answers. Therefore, in this paper, we research 1) to what extent does using chatbots improve higher education students’ essential knowledge of computers network? and 2) to what extent does using chatbots improve the self-efficacy of higher education students? Our first insight indicates that groundbreaking generalizable chatbots such as You and ChatGPT produce rather detailed answers to the questions associated with computer network courses, but those answers may not be the model answers that teachers expect from the students. Hence, research-based course-specific standard chatbots are required to teach computer network topics effectively.