Thinking Aloud with a Tutoring Robot to Enhance Learning

Ramachandran, Aditi; Huang, Chien-Ming; Gartland, Edward; Scassellati, Brian

doi:10.1145/3171221.3171250

Cited by 59 publications

(40 citation statements)

References 37 publications

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“…For example, Leyzberg et al [2012] found that the physical presence and embodiment of robots help improve cognitive learning gains during tutoring and Ramachandran et al [2018] found a positive effect of a physically embodied social robot on thinking aloud behavior in children. Furthermore, Zaga et al [2017] found that social gaze movements increase children's perception of animacy and likeablility.…”

Section: Discussionmentioning

confidence: 99%

“…Previous research has shown that social agents can enhance the learning process using various approaches. For example, Ramachandran et al [2018] found a positive effect of using a physically embodied social robot on thinking aloud behavior on children and Baxter et al [2017] found that children who work with a social robot that either displays personalized or non-perzonalized behavior can support children in their learning.…”

Section: Introductionmentioning

confidence: 99%

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Now We’re Talking

Wijnen

Davison

Reidsma

et al. 2019

J. Hum.-Robot Interact.

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This article presents a study in which we explored the effect of a social robot on the explanatory behavior of children (aged 6-10) while working on an inquiry learning task. In a comparative experiment, we offered children either a baseline Computer Aided Learning (CAL) system or the same CAL system that was supplemented with a social robot to verbally explain their thoughts to. Results indicate that when children made observations in an inquiry learning context, the robot was better able to trigger elaborate explanatory behavior. First, this is shown by a longer duration of explanatory utterances by children who worked with the robot compared to the baseline CAL system. Second, a content analysis of the explanations indicated that children who worked with the robot included more relevant utterances about the task in their explanation. Third, the content analysis shows that children made more logical associations between relevant facets in their explanations when they explained to a robot compared to a baseline CAL system. These results show that social robots that are used as extensions to CAL systems may be beneficial for triggering explanatory behavior in children, which is associated with deeper learning. 5:2 F. M. Wijnen et al.computer-supported learning and technology-supported education. Technology can often provide assignments and give feedback (right or wrong answer), and, in some situations, the technology is able to adapt the assignment's difficulty level to the learner. In recent years we have seen an emerging role for socially capable interactive learning technologies. Technologies such as virtual agents and robots are capable of interacting and engaging with the learner on a social level. Engaging the learner in social interaction opens up possibilities for providing richer task-related support and scaffolding.In this article, we are specifically interested in a robot's influence on the child's explanatory behavior in the context of inquiry learning.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Now We’re Talking

Wijnen

Davison

Reidsma

et al. 2019

J. Hum.-Robot Interact.

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“…These choices are also present in ITSs, but the explicit agentic nature of robots often introduces additional options and, at times, complications. Choosing an appropriate emotional support strategy based on the affective state of the child (36), assisting with a meta-cognitive learning strategy (37), deciding when to take a break (31), and encouraging appropriate help-seeking behavior (4) have all been shown to increase student learning gains. Combining these actions with appropriate gestures (38), appropriate and congruent gaze behavior (39), expressive behaviors and attention-guiding behaviors (11), and timely nonverbal behaviors (3) also positively affects student recall and learning.…”

Section: Technical Challenges Of Building Robot Tutorsmentioning

confidence: 99%

Social robots for education: A review

et al. 2018

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Social robots can be used in education as tutors or peer learners. They have been shown to be effective at increasing cognitive and affective outcomes and have achieved outcomes similar to those of human tutoring on restricted tasks. This is largely because of their physical presence, which traditional learning technologies lack. We review the potential of social robots in education, discuss the technical challenges, and consider how the robot's appearance and behavior affect learning outcomes.

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“…Many variables are potentially important to the effect of robots in class. Now we have shown the feasibility of carrying out long term studies, we should explore the details: how does the presentation of the robot, its physical embodiment [26,29], its social gaze [43], its appearance, its facial expressions [4], its personality, its life-like behaviour, its background story [28], and so forth, affect children's involvement with the task and robot in longer term deployments? This need not necessarily be the same as in short term and/or lab studies -so we need to start carrying out more long term in the wild studies to start exploring this next level of interesting questions, as has been so well argued by Jung and Hinds [15].…”

Section: Resultsmentioning

confidence: 99%

Working with a Social Robot in School

Davison

Wijnen

Charisi³

et al. 2020

Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

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Interactive learning technologies, such as robots, increasingly find their way into schools. However, more research is needed to see how children might work with such systems in the future. This paper presents the unsupervised, four month deployment of a Robot-Extended Computer Assisted Learning (RECAL) system with 61 children working in their own classroom. Using automatically collected quantitative data we discuss how their usage patterns and self-regulated learning process developed throughout the study. CCS CONCEPTS • Applied computing → Interactive learning environments; • Computer systems organization → Robotics; • Human-centered computing → Field studies; • Social and professional topics → Children; • Hardware → Sensors and actuators.

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Thinking Aloud with a Tutoring Robot to Enhance Learning

Cited by 59 publications

References 37 publications

Now We’re Talking

Now We’re Talking

Social robots for education: A review

Working with a Social Robot in School

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