Math reading comprehension: Comparing effectiveness of various conversation frameworks in an ITS
Abstract
Conversation based intelligent tutoring systems (ITSs) are highly effective at promoting learning across a wide range of domains. This is in part because these systems allow for the implementation of pedagogical strategies used by expert human tutors (e.g., self-reflection and deep-level reasoning questions). However, the various conversation frameworks used by these ITSs affect high domain knowledge students and low domain knowledge students differently. The experiment proposed in this paper will explore and test the added effectiveness of interactive dialogues and trialogues in learning Algebra I, utilized in a conversation based ITS. The experiment will compare learning across five conditions: (1) a static reading control condition, (2) a vicarious control dialogue condition with animated agents, (3) an interactive dialogue condition (i.e., human learner and tutor agent), (4) an interactive trialogue condition (i.e., human learner, tutor agent, and tutee agent) and (5) a vicarious monologue condition. This research will seek to answer questions concerning the effectiveness of dialogue and trialogue conversation environments in an Algebra 1 domain compared to vicarious learning, and whether trialogues provide an added benefit over dialogues within this domain.
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Recommended Citation
Shubeck, K., Fang, Y., & Hu, X. (2017). Math reading comprehension: Comparing effectiveness of various conversation frameworks in an ITS. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10331 LNAI, 617-620. https://doi.org/10.1007/978-3-319-61425-0_77
