Multiple pedagogical conversational agents to support learner-learner collaborative learning: Effects of splitting suggestion types

Abstract

This study experimentally investigated the design of effective interactions using pedagogical conversational agents (PCAs) in a learner-learner collaborative learning activity. While dyads engaged in a concept explanation task (explaining the mechanism of computer processing), PCAs served as facilitators and provided metacognitive suggestions to better improve learning performance. Previous studies have shown that learners who received several types of suggestions from multiple PCAs were motivated to produce effective explanations; this study then further explored the effects of using multiple PCAs in different roles, providing different types of facilitation. It was predicted that by using two different PCAs to offer suggestions with a delay, learners may be able to process information more efficiently, for example by paying closer attention to each type of suggestion. To investigate this possibility, two types of facilitating content, namely provision of metacognitive suggestions and advice on effective coordination, were each implemented into two role-playing PCAs, named the “explanation adviser” and the “communication adviser” respectively. The results show that when learners used PCAs playing different roles and offering suggestions corresponding to these roles, learners generated explanations related to the suggestions and improved performance (efficacy of explanations) in several areas, including learning performance, for example better understanding the concept and becoming able to explain it using a greater range of technical words. This study shows empirically how multiple PCAs can be effectively designed to implement roles yielding different types of suggestions. The advantages of using such methods and implementing such functions of PCAs are further discussed.