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Impact of Individual Differences on Affective Reactions to Pedagogical Agents Scaffolding [chapter]

Sébastien Lallé, Nicholas V. Mudrick, Michelle Taub, Joseph F. Grafsgaard, Cristina Conati, Roger Azevedo
2016 Lecture Notes in Computer Science  
However, there is limited understanding of how Pedagogical Agents (PAs) impact different students' emotions, what those emotions are, and whether this is modulated by students' individual differences (  ...  Students' emotions are known to influence learning and motivation while working with agent-based learning environments (ABLEs).  ...  agent responsible for prompting cognitive strategies).  ... 
doi:10.1007/978-3-319-47665-0_24 fatcat:6stanq2emvakhfrle4ie23yko4

Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system

Melissa C. Duffy, Roger Azevedo
2015 Computers in Human Behavior  
Implications for the design of pedagogical agents are also discussed.  ...  MANCOVA analyses revealed that students in the prompt and feedback condition deployed more SRL strategies and spent more time viewing relevant science material compared to students in the control condition  ...  To further promote effective learning, researchers have embedded pedagogical agents within computer-based environments to adaptively scaffold SRL by providing timely instructional prompts and or feedback  ... 
doi:10.1016/j.chb.2015.05.041 fatcat:hc4jwt4fpze5pbur6wni7cgiby

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

Jason M. Harley, Susanne P. Lajoie, Claude Frasson, Nathan C. Hall
2016 International Journal of Artificial Intelligence in Education  
of adaptive, positively-valenced emotions while interacting with advanced learning technologies.  ...  In particular, multiple strategies system developers may use to help learners experience positive emotions are mapped out, including those that require different amounts and types of information about  ...  The authors would like to thank Reinhard Pekrun and James Gross for their thoughts and feedback on similarities between their theory and model with regard to emotion regulation.  ... 
doi:10.1007/s40593-016-0126-8 fatcat:y5yzbcjyizdexekdpckc4hwzuq

Affective Pedagogical Agent in E-Learning Environment: A Reflective Analysis

Atasi Mohanty
2016 Creative Education  
In the real-life physical learning environment students' affective feedback is a major cue that human tutors use to constantly adapt their teaching strategies, so that learning would be most effective  ...  Thus, in e-learning environment it's a research issue to think about how to design an artificial affective tutor or pedagogical agent, who can respond to students' emotion and accordingly guide and motivate  ...  Thus, their defining roles are: 1) Adapt-A pedagogical agent evaluates the learner's understanding throughout the interaction, just as a human teacher would, and adapts the lesson plan accordingly.  ... 
doi:10.4236/ce.2016.74061 fatcat:g55jkbasujhspj3g4u46b672bi

Learner Modelling and Automatic Engagement Recognition with Robotic Tutors

Fotios Papadopoulos, Lee J. Corrigan, Aidan Jones, Ginevra Castellano
2013 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction  
Generally, our model will be used with a robotic tutor to support and enhance the experience of the learner by regulating pedagogical and empathetic interventions in a timely manner.  ...  Additionally, we propose the initial steps of the design of a suitable scenario for the learning task activity to allow the model to be tested on actual class material from UK curriculum based on teachers  ...  It is therefore essential to be able to identify the emotional effect of the intervention and the effect on the learning process.  ... 
doi:10.1109/acii.2013.137 dblp:conf/acii/PapadopoulosCJC13 fatcat:txayuk7s3nfxbgzzoiurqdv3me

AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring

Benjamin D. Nye, Arthur C. Graesser, Xiangen Hu
2014 International Journal of Artificial Intelligence in Education  
Next, we review three central themes in AutoTutor's development: human-inspired tutoring strategies, pedagogical agents, and technologies that support natural-language tutoring.  ...  The latter findings include the effectiveness of deep reasoning questions for tutoring multiple domains, of adapting to the affect of low-knowledge learners, of content over surface features such as voices  ...  This raises a few questions: what emotions are important to help tutors adapt, what data can help reliably detect such emotions, and who benefits from adapting to such emotions?  ... 
doi:10.1007/s40593-014-0029-5 fatcat:4ykacwg665dzlgrtgmwq3kb3ua

Examining the Predictive Relationship Between Personality and Emotion Traits and Learners' Agent-Direct Emotions [chapter]

Jason M. Harley, Cassia C. Carter, Niki Papaionnou, François Bouchet, Ronald S. Landis, Roger Azevedo, Lana Karabachian
2015 Lecture Notes in Computer Science  
experiencing as a result of interacting with two Pedagogical Agents (PAs -agent-directed emotions) in MetaTutor, an advanced multi-agent learning environment.  ...  Suggestions are provided for adapting PAs to support learners' (with certain characteristics) experience of positive emotions (e.g., enjoyment) and minimize their experience of negative emotions (e.g.,  ...  Prompts: Sam and Mary provide prompts to students to engage in SRL behaviours, such as making summaries and content evaluations.  ... 
doi:10.1007/978-3-319-19773-9_15 fatcat:o4ugux2jbfaahgnyska66z6tay

AutoTutor and affective autotutor

Sidney D'mello, Art Graesser
2012 ACM transactions on interactive intelligent systems (TiiS)  
pedagogical strategies to help students learn.  ...  with two animated pedagogical agents.  ... 
doi:10.1145/2395123.2395128 fatcat:f6rqwoomdvfzvg6mf6rkw34lt4

Enhancing socially shared regulation in collaborative learning groups: designing for CSCL regulation tools

Sanna Järvelä, Paul A. Kirschner, Ernesto Panadero, Jonna Malmberg, Chris Phielix, Jos Jaspers, Marika Koivuniemi, Hanna Järvenoja
2014 Educational technology research and development  
These (meta)cognitive, social, motivational, and emotional aspects related to being/becoming aware of how one learns alone and with others are for the most part neglected in traditional CSCL support.  ...  For effective computer supported collaborative learning (CSCL), socially shared regulation of learning (SSRL) is necessary.  ...  To solve this problem, pedagogical agents and adaptive system elements are recent innovations advancing the original notion of computer-based pedagogical tools.  ... 
doi:10.1007/s11423-014-9358-1 fatcat:6ryozda33fdmhfd5cnbqc33v2e

Towards a Conceptual Framework to Scaffold Self-regulation in a MOOC [chapter]

Gorgoumack Sambe, François Bouchet, Jean-Marc Labat
2018 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering  
This framework relies on the use of a virtual companion to provide metacognitive prompts and a visualization of indicators.  ...  MOOCs are part of the ecosystem of self-learning for which self-regulation is one of the pillars. Weakness of self-regulation skills is one of the key factors that contribute to dropout in a MOOC.  ...  Supported by a pedagogical agents, their positive impact on SRL and on affect and emotion has been demonstrated [1, 2, 4] .  ... 
doi:10.1007/978-3-319-72965-7_23 fatcat:2nk26dzl2rbfbp4obei3fbnmlu

Personalizing e-Learning. The Social Effects of Pedagogical Agents

Nicole C. Krämer, Gary Bente
2010 Educational Psychology Review  
Numerous studies have evaluated the effects of pedagogical agents on students' learning outcomes, but so far, beneficial effects have not been consistently demonstrated.  ...  concerning the functions of embodiment and nonverbal behavior, such as modeling, discourse and dialogue functions, and socio-emotional effects.  ...  Summary of Research on the Effects of Pedagogical Agents In recent years, several reviews on pedagogical agent research summed up the results of evaluation studies (Baylor 2001; Clarebout et al. 2002;  ... 
doi:10.1007/s10648-010-9123-x fatcat:nkw5ouecqfdofjhfqjvqspeami

Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners' levels of prior knowledge in hypermedia-learning environments?

Michelle Taub, Roger Azevedo, François Bouchet, Babak Khosravifar
2014 Computers in Human Behavior  
These results have important implications on designing multi-agent, hypermedia environments; we can design pedagogical agents that adapt to students' learning needs, based on their prior knowledge levels  ...  One hundred twelve (N = 112) undergraduate students' interactions with MetaTutor, a multi-agent, hypermedia-based learning environment, were investigated, including how prior knowledge affected their use  ...  The findings from this study will help us design multi-agent systems with pedagogical agents that can adapt their decision making for students based on the students' levels of prior knowledge.  ... 
doi:10.1016/j.chb.2014.07.018 fatcat:us2tiunglvbwldpde4iiwpyod4

Intelektinė daugiaagentė mokymosi sistema, naudojanti edukacinių duomenų tyrybą

Eugenijus Kurilovas, Jaroslav Meleško, Irina Krikun
2018 Informacijos mokslai  
Harley et al. (2016) examined the predictive effects of learners' trait emotions and personality traits on agent-directed emotions.  ...  Intelligent agents can adapt learning materials to the different learning styles of students and leverage innate pre-dispositions for knowledge acquisition on intellectual, sensory and emotional levels  ... 
doi:10.15388/im.2017.79.11381 fatcat:vdtxf3uctbfx7cp7jfhzqmqe3u

Let Me Guide You! Pedagogical Interaction Style For A Robot In Children'S Education

Rifca Peters, Joost Broekens, Mark A. Neerincx
2015 Zenodo  
While human educators heavily rely on their ability to identify and respond accordingly to social signals in a fluent and natural way, robots cannot adapt their style of interaction effectively.  ...  Pedagogical Agents (PAs) are being developed to adapt to the users knowledge, and efforts are made in strategic action selection: what action is appropriate given the context and user preference.  ...  Some have investigated generalized effect of educational strategies on the interaction itself and outcomes such as learning gains [3, 24, 28] .  ... 
doi:10.5281/zenodo.166681 fatcat:lbar2uyh6rcejicshwkpn3lxgi

Clustering and Profiling Students According to their Interactions with an Intelligent Tutoring System Fostering Self-Regulated Learning

Francois Bouchet, Jason M. Harley, Gregory J. Trevors, Roger Azevedo
2013 Zenodo  
We conclude with a discussion of implications for designing a more adaptive ITS based on an identification of learners' profiles  ...  In this paper, we present the results obtained using a clustering algorithm (Expectation-Maximization) on data collected from 106 college students learning about the circulatory system with MetaTutor,  ...  INTRODUCTION A major challenge for researchers and developers of agent-based ITSs is how best to adapt to learners in order to provide individualized instruction i.e., for pedagogical agents (PAs) to adapt  ... 
doi:10.5281/zenodo.3554613 fatcat:eaeu335si5hjnobd26xrw5336a
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