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UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society Title Pedagogical agents that support learning by explaining: Effects of affective feedback Publication Date Pedagogical agents that support learning by explaining: Effects of affective feedback

Yugo Hayashi, Mariko Matsumoto, Hitsohi Ogawa, Yugo Hayashi, Mariko Matsumoto, Hitoshi Ogawa
2012 Proceedings of the Annual Meeting of the Cognitive Science Society   unpublished
Results of an experiment suggested that affective positive feedbacks from conversational agent facilitate explanation and learning performance.  ...  It is discussed that a conversational agent can play a role for pedagogical tutoring and triggers a deeper understanding of a concept during an explanation.  ...  Pedagogical agent can play several different roles for collaborative learning activities and several studies have looked into the effectiveness of the use of a pedagogical agent with different roles.  ... 

Elements Explaining Learning Clinical Reasoning Using Simulation Games

Jaana-Maija Koivisto, Elina Haavisto, Hannele Niemi, Jouko Katajisto, Jari Multisilta
2016 International Journal of Serious Games  
Findings also revealed that authentic patient-related experiences, feedback, and reflection have an indirect effect on learning clinical reasoning.  ...  The findings showed that usability, application of nursing knowledge, and exploration have the most impact on learning clinical reasoning when playing simulation games.  ...  Acknowledgements The authors would like to thank Anna-Saida Koskiluoma, Tuomas Louhelainen and Saku Nylund for their contribution to the development of the CareMe simulation game.  ... 
doi:10.17083/ijsg.v3i4.136 fatcat:4zfuza6fnjgkzdpwfe272q53zu

Explainable Goal-Driven Agents and Robots – A Comprehensive Review [article]

Fatai Sado, Chu Kiong Loo, Wei Shiung Liew, Matthias Kerzel, Stefan Wermter
2021 arXiv   pre-print
Finally, the paper presents requirements for explainability and suggests a roadmap for the possible realization of effective goal-driven explainable agents and robots.  ...  The review highlights key strategies that emphasize transparency, understandability, and continual learning for explainability.  ...  Acknowledgment This research was supported by the Georg Forster Research Fellowship for Experienced Researchers from Alexander von Humboldt-Stiftung/Foundation and Impact Oriented Interdisciplinary Research  ... 
arXiv:2004.09705v7 fatcat:p5jxv5hfk5elphzre4cn6acgsa

Explainable Artificial Intelligence: a Systematic Review [article]

Giulia Vilone, Luca Longo
2020 arXiv   pre-print
This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models but lack explainability and interpretability.  ...  This systematic review contributes to the body of knowledge by clustering these methods with a hierarchical classification system with four main clusters: review articles, theories and notions, methods  ...  for agents that autonomously learn, for instance how to play video-games.  ... 
arXiv:2006.00093v4 fatcat:dr26wgxvqrg7diljklhmdjkj7i

Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities [article]

Waddah Saeed, Christian Omlin
2021 arXiv   pre-print
In response to this need, Explainable AI (XAI) has been proposed to make AI more transparent and thus advance the adoption of AI in critical domains.  ...  However, this success has been met by increasing model complexity and employing black-box AI models that lack transparency.  ...  ., users affected by the model decision), modeling experts (e.g., data scientists).  ... 
arXiv:2111.06420v1 fatcat:q2nnfrenyvg3lpmu4qrug2m2zq

In Search of Embodied Conversational and Explainable Agents for Health Behaviour Change and Adherence

Amal Abdulrahman, Deborah Richards
2021 Multimodal Technologies and Interaction  
The article also introduces ECAs who provide explanations of their recommendations, known as explainable agents (XAs), as a way to build trust and enhance the working alliance towards improved behavior  ...  Of particular promise, is work in which XAs are able to engage in conversation to learn about their user and personalize their recommendations based on their knowledge of the user and then tailor their  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/mti5090056 fatcat:fv3mo6t4g5ggroohmcvnjn5kny

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI [article]

Shane T. Mueller, Robert R. Hoffman, William Clancey, Abigail Emrey, Gary Klein
2019 arXiv   pre-print
The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems).  ...  Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted.  ...  (explicability of AI-generated task plans) and transparent agents for strategy gameplay supported by deep networks.  ... 
arXiv:1902.01876v1 fatcat:glklvdwc2rbdzgteew4ttfjz2q

Explaining Behavior Through Observational Investigation and Theory Articulation

Brian K. Smith, Brian J. Reiser
2005 The Journal of the Learning Sciences  
We focus our discussion on an investigation model that scaffolds students through the processes of observing and explaining video as data and the computational and curricular supports that were designed  ...  We conclude with a presentation of preliminary results to illustrate the types of explanations that emerged from working with the software and curriculum and a discussion of issues that emerged during  ...  and feedback.  ... 
doi:10.1207/s15327809jls1403_1 fatcat:zinpsxqayve3hdmf5wzszpeiv4

Social brain or institutions, cultural tools and social practices? How to explain school processes and inequalities?

Jean-Yves Rochex
2016 Éducation et didactique  
, as represented by the concept of the 'social brain'.  ...  In this article, I discuss this concept, its value for psychological studies of teaching and learning, and how it can be related to a sociocultural theory of education and cognitive development.  ...  affect the course of that development.  ... 
doi:10.4000/educationdidactique.2522 fatcat:43ebbyhynvfmtelnuilpuxus6a


PhD Victor Oluwatosin Ajayi
2020 Figshare  
This research investigated the effects of Predict-Explain-Observe-Explain (PEOE) and Vee Heuristic (VH) strategies on students' achievement, metacognitive awareness and self-efficacy belief in Organic  ...  The moderating effect of gender was also investigated. The study adopted a pretest, posttest, control group, quasi-experimental research design.  ...  Learning is a personal and unique experience that differs from individual to individual and it can be enhanced by collaborative learning, which is regarded as a powerful pedagogical process that fosters  ... 
doi:10.6084/m9.figshare.12205043 fatcat:7lroan7c2ve2pozntvqw4v6fk4

Harnessing value from data science in business: ensuring explainability and fairness of solutions [article]

Krzysztof Chomiak, Michał Miktus
2021 arXiv   pre-print
The paper introduces concepts of fairness and explainability (XAI) in artificial intelligence, oriented to solve a sophisticated business problems.  ...  of future research avenues.  ...  Currently, it supports both binary and multi-class classification, as well as the regression problems, permitting users to explore the effects of alternative thresholds and fairness criteria.  ... 
arXiv:2108.07714v1 fatcat:s36ftwpzyvbaxnawhtcdtpyobe

Toward an understanding of "teaching in the making": Explaining instructional decision making by analyzing a geology instructor's use of metaphors

Glenn Robert Dolphin
2016 Geosphere  
Maintaining a similar focus, this investigation analyzes a geology instructor's use of metaphor, when talking about teaching, learning, and knowledge, to understand and explain the factors involved in  ...  This study suggests that curriculum designers need to take instructor context into consideration when designing curricular interventions and analyzing for the use of metaphor may be an effective way to  ...  Barnett and Hodson said that teachers need to understand their pedagogical context knowledge for them to become more effective.  ... 
doi:10.1130/ges01202.1 fatcat:kqwqystxhzdn3g267vsvyewxhm

Interactive Pedagogical Agents for Learning Sequence Diagrams [chapter]

Sohail Alhazmi, Charles Thevathayan, Margaret Hamilton
2020 Lecture Notes in Computer Science  
Our pedagogical agent combining data dependencies and quality metrics with rule-based techniques capturing consistency constraints allowed generation of immediate and holistic feedback.  ...  Providing manual timely feedback, though effective, cannot scale for large classes.  ...  Related Work Pedagogical agents are defined to be autonomous agents that support human learning, by interacting with students in the context of an interactive learning environment as a guide, critic, coach  ... 
doi:10.1007/978-3-030-52240-7_2 fatcat:3ltye72jfvbwzegadpw5iq3b2i

On Pedagogical Effects of Learner-Support Agents in Collaborative Interaction [chapter]

Yugo Hayashi
2012 Lecture Notes in Computer Science  
The findings of the experiments suggested that (1) a conversational agent can facilitate a deeper understanding of conceptwhen participants are attentive to its presence, and (2) affective positive feedbacks  ...  The present study was conducted to investigate if and how conversational agent can facilitate explanation activity that is conducive to learning.  ...  The Effects of Affective Feedback.  ... 
doi:10.1007/978-3-642-30950-2_3 fatcat:tnw3souvyvfhjd7duaxyzuvwo4

Explainable AI for B5G/6G: Technical Aspects, Use Cases, and Research Challenges [article]

Shen Wang, M.Atif Qureshi, Luis Miralles-Pechuaán, Thien Huynh-The, Thippa Reddy Gadekallu, Madhusanka Liyanage
2021 arXiv   pre-print
The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process.  ...  Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems.  ...  The agent gets positive feedback when the taken action leads to a reward, and negative feedback when Fig. 3.  ... 
arXiv:2112.04698v1 fatcat:y7ss4opmrjbsbjm3ip2vgkkgky
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