Animated Pedagogical Agents: Face-to-Face Interaction in Interactive Learning Environments

By W. L. Johnson, Jeff Rickel, James C. Lester. 2000

1,214 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d5ca5efdecba2707aab57d4cb2df008a7ecdd0dd

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Citations in the corpus

Do pedagogical agents make a difference to student motivation and learning (2011, background): “While their supporters expect them to foster learner motivation and learning outcomes due to the social cues they provide (e.g., Atkinson, 2002; Baylor & Kim, 2005; Craig et al., 2002; Johnson et al., 2000), others express concerns that they may distract the learner from the learning content (Dehn &”
AutoTutor: A tutor with dialogue in natural language (2004, methodology): “Syntactic tagging is provided by the Apple Pie parser (Sekine & Grishman, 1995) together with cascaded finite state transducers (see Jurafsky & Martin, 2000).”
Where to look: a study of human-robot engagement (2004, background): “For 2D conversational agents, researchers (notably, [5],[7]) have explored agents that produce gestures in conversation.”
Expressive robots in education: varying the degree of social supportive behavior of a robotic tutor (2010, background): “Animated pedagogical agents have been proposed as a promising interface paradigm that allows human-like social interaction due to their ability to give non-verbal and emotional feedback [20].”
AutoTutor: an intelligent tutoring system with mixed-initiative dialogue (2005, background): “Examples of these systems are Steve [23] and Mission Rehearsal [24].”
A review of the applicability of robots in education (2013, background): “Other attributes such as maintaining eye contact have also been discussed [25] to engage students.”
Redefining the learning companion: the past, present, and future of educational agents (2003, background): “A significant advance is the development of animated pedagogical agents ( Johnson, Rickel, & Lester, 2000 ).”
A Social-Cognitive Framework for Pedagogical Agents as Learning Companions (2006, background): “Likewise, pedagogical agents as simulated beings are frequently designed to represent different human instructional roles, such as expert (Johnson et al., 2000), tutor (Graesser, Moreno, & Marineau, 2003), mentor (Baylor & Kim, 2005), and learning companion (Chan & Baskin, 1990; Dillenbourg & Self,…”
Optimizing learning from examples using animated pedagogical agents. (2002, background): “In particular, it may be possible to structure an example-based learning environment so that a lifelike character can exploit verbal (e.g., instructional explanations) as well as nonverbal forms of communication (e.g., gaze, gesture) within the examples themselves in an effort to promote a learner’s”
Pedagogical Agents: Back to the Future (2018, unknown): “Building on this work, in 2000, together with our colleague Jeff Rickel, we published an article on pedagogical agents (Johnson, Rickel, and Lester 2000) that surveyed and discussed the potential of this new paradigm.”
Animated Pedagogical Agents in Multimedia Educational Environments: Effects of Agent Properties, Picture Features, and Redundancy (2002, background): “Lester et al. compared the fully expressive Herman with four other versions (see Johnson et al., 2000, p. 61): spoken principle-based advice only, high-level spoken advice only, spoken task-specific advice only, and a version that was mute with respect to plant components.”
Scaffolding Deep Comprehension Strategies Through Point&Query, AutoTutor, and iSTART (2005, background): “…could have different versions that systematically manipulate whether or not the animated conversational agents (a) present suitable challenges to the learner, (b)model illuminating inquiryprocesses, and (c) give feedback to learners who drift too far away from the critical paths in the hyperspace.”
Fostering social agency in multimedia learning: Examining the impact of an animated agent’s voice ☆ (2005, background): “It has been proposed that the combination of an interesting animated agent and a well-structured learning environment can optimize a learner s active engagement with the task and increase the probability of future interactions with the instructional program (Johnson et al., 2000).”
A review of verbal and non-verbal human-robot interactive communication (2014, background): “…towards affective human-robot interaction includes [101] where, extending upon analogous research from virtual avatars such as Rea [102], Steve [103], and Greta [104], Cynthia Breazeal presents an interactive emotion and drive system for the Kismet robot [105], which is capable of multiple…”