Towards Academically Productive Talk Supported by Conversational Agents

Dyke, Grégory; Adamson, David; Howley, Iris; Rosé, Carolyn Penstein

doi:10.1007/978-3-642-30950-2_69

Cited by 20 publications

(22 citation statements)

References 6 publications

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“…These virtual tutors take the physical appearance of everything from text-only agents in a chat room [5]to animated and threedimensional characters [13]. Many of the benefits of animated pedagogical agents, including increases in student engagement, motivation, and increased nonverbal communication, can be anticipated for pedagogical robots as well, perhaps even more so.…”

Section: Educational Robotsmentioning

confidence: 99%

Effects of social presence and social role on help-seeking and learning

Howley¹,

Kanda²,

Hayashi³

et al. 2014

Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction

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The unique social presence of robots can be leveraged in learning situations to reduce student evaluation anxiety, while still providing instructional guidance. Furthermore, social role of the instructor can also impact the prevalence of evaluation apprehension. In this study, we examine how human and robot social role affects help-seeking behaviors and learning outcomes in a one-on-one tutoring setting. Our results show that helpseeking is a moderator of the relationship between condition and learning, with the "human teacher" condition resulting in significantly less learning (and marginally less help-seeking) than the "human assistant" and both robot conditions.

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Section: Educational Robotsmentioning

confidence: 99%

Effects of social presence and social role on help-seeking and learning

Howley¹,

Kanda²,

Hayashi³

et al. 2014

Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction

Self Cite

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“…Currently, systematic message-coding schemas are combined with learning analytics to develop a more profound understanding of message quality. For example, Dyke et al [26] demonstrated that off-topic messages are more harmful to discussions that focus on learning basic facts than during discussions of problem-solving activities. Moreover, Wise et al [27] showed a relationship between the time students take to read and re-read existing messages and the quality of new messages in AODs.…”

Section: Page 43mentioning

confidence: 99%

Integrating Learning Analytics to Measure Message Quality in Large Online Conversations

Eryilmaz

Thoms

Ahmed

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Research on computer-supported collaborative learning often employs content analysis as an approach to investigate message quality in asynchronous online discussions using systematic message-coding schemas. Although this approach helps researchers count the frequencies by which students engage in different socio-cognitive actions, it does not explain how students articulate their ideas in categorized messages. This study investigates the effects of a recommender system on the quality of students' messages from voluminous discussions. We employ learning analytics to produce a quasi-quality index score for each message. Moreover, we examine the relationship between this score and the phases of a popular message-coding schema. Empirical findings show that a custom CSCL environment extended by a recommender system supports students to explore different viewpoints and modify interpretations with higher quasi-quality index scores than students assigned to the control software. Theoretical and practical implications are also discussed.

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“…These, in turn, can provide a team of learners with metacognitive prompts aimed at supporting collaborative problem-solving skills. Recent work shows that conversational agents can facilitate learning processes by guiding learners through their reasoning in a tutoring session (Dyke, Adamson, Howley, & Rose´, 2012). For example, research based on the Academically Productive Talk (APT) methodology, which stresses the importance of social interaction in supporting learning processes, has shown that interventions can scaffold learning and performance.…”

Section: Communication and Conversational Agents In Itssmentioning

confidence: 99%

“…For example, research based on the Academically Productive Talk (APT) methodology, which stresses the importance of social interaction in supporting learning processes, has shown that interventions can scaffold learning and performance. In particular, agents used in these studies were designed to facilitate APT by means of generating novel dialog and scaffolding the interactions that produce such dialogs (Adamson, Ashe, Jang, Yaron, & Rose´, 2013;Dyke et al, 2012). To do this, the agent might offer an individual learner a prompt via a chat log that directs them to contribute to an ongoing discussion.…”

Section: Communication and Conversational Agents In Itssmentioning

confidence: 99%

Building Intelligent Tutoring Systems for Teams

Johnston¹

2018

Research on Managing Groups and Teams

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Team cognition research continues to evolve as the need for understanding and improving complex problem solving itself grows. Complex problem solving requires members to engage in a number of complicated collaborative processes to generate solutions. This chapter illustrates how the Macrocognition in Teams model, developed to guide research on these processes, can be utilized to propose how intelligent tutoring systems (ITSs) could be developed to train collaborative problem solving. Metacognitive prompting, based upon macrocognitive processes, was offered as an intervention to scaffold learning these complex processes. Our objective is to provide a theoretically grounded approach for linking intelligent tutoring research and development with team cognition. In this way, team members are more likely to learn how to identify and integrate relevant knowledge, as well as plan, monitor, and reflect on their problem-solving performance as it evolves. We argue that ITSs that utilize metacognitive prompting that promotes team planning during the preparation

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Towards Academically Productive Talk Supported by Conversational Agents

Cited by 20 publications

References 6 publications

Effects of social presence and social role on help-seeking and learning

Effects of social presence and social role on help-seeking and learning

Integrating Learning Analytics to Measure Message Quality in Large Online Conversations

Building Intelligent Tutoring Systems for Teams

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