Using a Chatbot to Provide Formative Feedback: A Longitudinal Study of Intrinsic Motivation, Cognitive Load, and Learning Performance

• Published in: IEEE transactions on learning technologies Vol. 17; pp. 1378 - 1389
• Main Authors: Yin, Jiaqi, Goh, Tiong-Thye, Hu, Yi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anxiety; Anxiety disorders; Chatbot; Chatbots; Cognitive load; Data analysis; Design of experiments; Educational objectives; Feedback; formative feedback; intrinsic motivation; learning performance; Motivation; Search problems; Students; Task analysis; Teachers; Undergraduate study; Videos; Writing
• ISSN: 1939-1382; 2372-0050
• DOI: 10.1109/TLT.2024.3364015

This study aimed to examine sustainable effects of chatbot-based formative feedback on intrinsic motivation, cognitive load, and learning performance. A longitudinal quasi-experimental design with 173 undergraduate students was conducted. The experiment is a between-subject design. Students either received formative feedback from a chatbot or a teacher. Utilizing linear mixed model and t-test for data analysis, results showed the following. First, chatbot-based feedback resulted in increased learning interest, perceived choice, and value while decreasing perceived pressure over time. Second, chatbot-based feedback was effective in reducing cognitive load, particularly when learning contents involved conceptual or difficult knowledge. Finally, chatbot-based feedback was found to be more efficient and effective in supporting the mastery of application-based knowledge compared with teacher-based feedback. This study has practical implications for the design of chatbots, and it also enriches the methods of providing ongoing formative feedback in large-scale classrooms.