Teachable agents are pedagogical agents that employ the ‘learning-by-teaching’ strategy, which facilitates learning by encouraging students to construct explanations, reflect on misconceptions, and elaborate on what they know. Teachable agents present unique opportunities to maximize the benefits of a ‘learning-by-teaching’ experience. For example, teachable agents can provide socio-emotional support to learners, influencing learner self-efficacy and motivation, and increasing learning. Prior work has found that a teachable agent which engages learners socially through social dialogue and paraverbal adaptation on pitch can have positive effects on rapport and learning. In this work, we introduce Emma, a teachable robotic agent that can speak socially and adapt on both pitch and loudness. Based on the phenomenon of entrainment, multi-feature adaptation on tone and loudness has been found in human-human interactions to be highly correlated to learning and social engagement. In a study with 48 middle school participants, we performed a novel exploration of how multi-feature adaptation can influence learner rapport and learning as an independent social behavior and combined with social dialogue. We found significantly more rapport for Emma when the robot both adapted and spoke socially than when Emma only adapted and indications of a similar trend for learning. Additionally, it appears that an individual’s initial comfort level with robots may influence how they respond to such behavior, suggesting that for individuals who are more comfortable interacting with robots, social behavior may have a more positive influence.
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Comfort with Robots Influences Rapport with a Social, Entraining Teachable Robot
Teachable agents are pedagogical agents that employ the 'learning-by-teaching' strategy, which facilitates learning by encouraging students to
construct explanations, reflect on misconceptions, and elaborate on what they know. Teachable agents present unique opportunities to maximize the benefits of a 'learning-by-teaching' experience. For example, teachable agents can provide socio-emotional support to learners, influencing learner self-efficacy and motivation, and increasing learning. Prior work has found that a teachable agent which engages learners socially through social dialogue and paraverbal adaptation on pitch can have positive effects on rapport and learning. In this work, we introduce Emma, a teachable robotic agent that can speak socially and adapt on both pitch and loudness. Based on the phenomenon of entrainment, multi-feature adaptation on tone and loudness has been found in human-human interactions to be highly correlated to learning and social engagement. In a study with 48 middle school participants, we performed a novel exploration of how multi-feature adaptation can influence learner rapport and learning as an independent social behavior and combined with social dialogue. We found significantly more rapport for Emma when the robot both adapted and spoke socially than when Emma only adapted and indications of a similar trend for learning. Additionally, it appears that an individual’s initial comfort level with robots may influence how they respond to such behavior, suggesting that for individuals who are more comfortable interacting with robots, social behavior may have a more positive influence.
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- Award ID(s):
- 1637947
- NSF-PAR ID:
- 10127933
- Date Published:
- Journal Name:
- Proceedings of Artificial Intelligence in Education
- Page Range / eLocation ID:
- 231-243
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Pedagogical agents have the potential to provide not only cognitive support to learners but socio-emotional support through social behavior. Socioemotional support can be a critical element to a learner’s success, influencing their self-efficacy and motivation. Several social behaviors have been explored with pedagogical agents including facial expressions, movement, and social dialogue; social dialogue has especially been shown to positively influence interactions. In this work, we explore the role of paraverbal social behavior or social behavior in the form of paraverbal cues such as tone of voice and intensity. To do this, we focus on the phenomenon of entrainment, where individuals adapt their paraverbal features of speech to one another. Paraverbal entrainment in human-human studies has been found to be correlated with rapport and learning. In a study with 72 middle school students, we evaluate the effects of entrainment with a teachable robot, a pedagogical agent that learners teach how to solve ratio problems. We explore how a teachable robot which entrains and introduces social dialogue influences rapport and learning; we compare with two baseline conditions: a social condition, in which the robot speaks socially, and a non-social condition, in which the robot neither entrains nor speaks socially. We find that a robot that does entrain and speaks socially results in significantly more learning.more » « less
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Abstract Background Providing adaptive scaffolds to help learners develop effective self‐regulated learning (SRL) behaviours has been an important goal for intelligent learning environments. Adaptive scaffolding is especially important in open‐ended learning environments (OELE), where novice learners often face difficulties in completing their learning tasks.
Objectives This paper presents a systematic framework for adaptive scaffolding in Betty's Brain, a learning‐by‐teaching OELE for middle school science, where students construct a causal model to teach a virtual agent, generically named Betty. We evaluate the adaptive scaffolding framework and discuss its implications on the development of more effective scaffolds for SRL in OELEs.
Methods We detect key cognitive/metacognitive
inflection points , that is, moments where students' behaviours and performance change during learning, often suggesting an inability to apply effective learning strategies. At inflection points, Mr. Davis (a mentor agent in Betty's Brain ) or Betty (the teachable agent ) provides context‐specific conversational feedback, focusing on strategies to help the student become a more productive learner, or encouragement to support positive emotions. We conduct a classroom study with 98 middle schoolers to analyse the impact of adaptive scaffolds on students' learning behaviours and performance. We analyse how students with differential pre‐to‐post learning outcomes receive and use the scaffolds to support their subsequent learning process in Betty's Brain.Results and Conclusions Adaptive scaffolding produced mixed results, with some scaffolds (viz., strategic hints that supported debugging and assessment of causal models) being generally more useful to students than others (viz., encouragement prompts). Additionally, there were differences in how students with high versus low learning outcomes responded to some hints, as suggested by the differences in their learning behaviours and performance in the intervals after scaffolding. Overall, our findings suggest how adaptive scaffolding in OELEs like Betty's Brain can be further improved to better support SRL behaviours and narrow the learning outcomes gap between high and low performing students.
Implications This paper contributes to our understanding and impact of adaptive scaffolding in OELEs. The results of our study indicate that successful scaffolding has to combine context‐sensitive inflection points with conversational feedback that is tailored to the students' current proficiency levels and needs. Also, our conceptual framework can be used to design adaptive scaffolds that help students develop and apply SRL behaviours in other computer‐based learning environments.
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As dialogue systems become more prevalent in the form of personalized assistants, there is an increasingly important role for systems which can socially engage the user by influencing social factors like rapport. For example, learning companions enhance learning through socio-motivational support and are more successful when users feel rapport. In this work, I explore social engagement in dialogue systems in terms of acoustic-prosodic entrainment; entrainment is a phenomenon where over the course of a conversation, speakers adapt their acoustic-prosodic features, becoming more similar in their pitch, intensity, or speaking rate. Correlated with rapport and task success, entrainment plays a significant role in how individuals connect; a system which can entrain has potential to improve social engagement by enhancing these factors. As a result of this work, I introduce a dialogue system which can entrain and investigate its effects on social factors like rapport.more » « less
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Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been shown to promote learning. Past work on lexical alignment in the field of education suffers from limitations in both the measures used to quantify alignment and the types of interactions in which alignment with agents has been studied. In this paper, we apply alignment measures based on a data-driven notion of shared expressions (possibly composed of multiple words) and compare alignment in one-on-one human-robot (H-R) interactions with the H-R portions of collaborative human-human-robot (H-H-R) interactions. We find that students in the H-R setting align with a teachable robot more than in the H-H-R setting and that the relationship between lexical alignment and rapport is more complex than what is predicted by previous theoretical and empirical work.more » « less
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