An Internet Articles Retrieval Agent Combined With Dynamic Associative Concept Maps to Implement Online Learning in an Artificial Intelligence Course

• Published in: International review of research in open and distance learning 2022-02, Vol.23 (1), p.63-81
• Main Authors: Cheng, Yu-Ping, Cheng, Shu-Chen, Huang, Yueh-Min
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Classrooms; Cognitive load; Cognitive Processes; College Students; Computer Science; Computer Science Education; Concept Mapping; Control Groups; Critical thinking; Difficulty Level; Distance learning; dynamic associative concept maps; Educational Environment; Educational materials; Educational objectives; Electronic Learning; Experiments; Foreign Countries; Higher education; Information Retrieval; intelligent agent; Internet; Knowledge; Learning Activities; Learning Processes; Multimedia; Online Courses; Online instruction; online learning; Online Searching; Real time; Science Curriculum; Search engines; Student Motivation; Students; Teaching Methods; text mining
• ISSN: 1492-3831; 1492-3831
• DOI: 10.19173/irrodl.v22i4.5437

Online learning has been widely discussed in education research, and open educational resources have become an increasingly popular way to help learners acquire knowledge. However, these resources contain massive amounts of information, making it difficult for learners to identify Web articles that refer to computer science knowledge. This study developed an Internet articles retrieval agent combined with dynamic associative concept maps (DACMs). The system used text mining technology to analyze keywords to filter computer science articles. In previous research, concept maps were manually constructed; in this study, such maps can be automatically and dynamically generated in real time. In a case study of a fundamental course of artificial intelligence, this study designed two experiments to compare students’ learning behaviors while using this system and the Google search engine. The results of the first experiment showed that the experimental group searched for more knowledge articles on computer science using this agent, compared to the control group using the Google search engine. The learning performance of the experimental group was significantly better than that of the control group, while the cognitive load of the experimental group was significantly lower than that of the control group. Furthermore, the results of the second experiment showed that the learning progress of students using the agent was significantly greater than that of students who used the Google search engine. This illustrates that the agent effectively filtered computer science articles, and DACMs helped students gain a deeper understanding of academic concepts and knowledge related to artificial intelligence.