Investigating the Relationship Between Dialogue States and Partner Satisfaction During Co-Creative Learning Tasks

• Published in: International journal of artificial intelligence in education 2023-09, Vol.33 (3), p.543-582
• Main Authors: Griffith, Amanda E., Katuka, Gloria Ashiya, Wiggins, Joseph B., Boyer, Kristy Elizabeth, Freeman, Jason, Magerko, Brian, McKlin, Tom
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Browsing; Coding; Cognitive tasks; Collaborative learning; Computation; Computer Science; Computers and Education; Cooperative Learning; Core curriculum; Creative Activities; Creative process; Creativity; Curricula; Dialogs (Language); Editing; Educational Technology; Error Patterns; High School Students; Individualized Instruction; Knowledge; Learning; Markov chains; Markov Processes; Music; Outcomes of Education; Predictor Variables; Problem solving; Scaffolding; Science Curriculum; Science education; Scores; Secondary school students; Secondary schools; Student Interests; Student Satisfaction; Thinking Skills; User Interfaces and Human Computer Interaction
• ISSN: 1560-4292; 1560-4306
• DOI: 10.1007/s40593-022-00302-5

Collaborative learning offers numerous benefits to learners, largely due to the dialogue that is unfolding between them. However, there is still much to learn about the structure of collaborative dialogue, and especially little is known about co-creative dialogues during learning. This paper reports on a study with learners engaged in co-creative tasks where the learners wrote code to create a song and while engaging in textual dialogue as they did so. After gathering the textual dialogue and the actions within the interface, we learned a hidden Markov model (HMM) to reveal co-creative states. The seven-state model revealed four states primarily composed of coding actions that included browsing the curriculum documents, working in the code editor, compiling the code successfully, and receiving a compile error. The remaining three states are primarily composed of dialogue that can be characterized as social, aesthetic, and technical dialogue. Next, we analyzed the relationships between the co-creative states revealed by the HMM and students’ partner satisfaction scores from a post-survey. The results reveal the relative frequency of actions in certain states and some transitions between states were predictive of partner satisfaction. For example, partner satisfaction was negatively associated with the Compilation Error state and with the relative frequency of transitions from the Curriculum Browsing state to the Code Editing state. Partner satisfaction was also negatively associated with the relative frequency of transitions from the Aesthetic Dialogue state to the Technical Dialogue state and the Code Editing state. This line of investigation reveals how co-creative processes are associated with partner satisfaction, and holds the potential to inform scaffolding for collaborative learning.