Alle Publikationen
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2018
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(2018): Ms An (meeting students’ academic needs): A socially adaptive robot tutor for student engagement in math education. ProQuest Information & Learning.
Abstract: This research presents a new, socially adaptive robot tutor, Ms. An ( Meeting Students’ Academic N eeds). The goal of this research was to use a decision tree model to develop a socially adaptive robot tutor that predicted and responded to student emotion and performance to actively engage students in mathematics education. The novelty of this multi-disciplinary project is the combination of the fields of HRI, AI, and education. In this study we 1) implemented a decision tree model to classify student emotion and performance for use in adaptive robot tutoring-an approach not applied to educational robotics; 2) presented an intuitive interface for seamless robot operation by novice users; and 3) applied direct human teaching methods (guided practice and progress monitoring) for a robot tutor to engage students in mathematics education. Twenty 4th and 5th grade students in rural South Carolina participated in a between subjects study with two conditions: A) with a non-adaptive robot (control group); and B) with a socially adaptive robot (adaptive group). Students engaged in two one-on-one tutoring sessions to practice multiplication per the South Carolina 4th and 5 th grade mathematics state standards. Although our decision tree models were not very predictive, the results gave answers to our current questions and clarity for future directions. Our adaptive strategies to engage students academically were effective. Further, all students enjoyed working with the robot and we did not see a difference in emotional engagement across the two groups. This study offered insight for developing a socially adaptive robot tutor to engage students academically and emotionally while practicing multiplication. Results from this study will inform the human-robot interaction (HRI) and artificial intelligence (AI) communities on best practices and techniques within the scope of this work. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
2014
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(2014): Robots show us how to teach them: feedback from robots shapes tutoring behavior during action learning. In: PLoS one 9 (3). DOI: 10.1371/journal.pone.0091349
DOI: https://doi.org/10.1371/journal.pone.0091349 Abstract: Robot learning by imitation requires the detection of a tutor's action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influences the human tutor's movement demonstrations in the process of action learning. We argue that the robot's feedback strongly shapes how tutors signal what is relevant to an action and thus advocate a paradigm shift in robot action learning research toward truly interactive systems learning in and benefiting from interaction.
Keywords: Action Learning, Artificial intelligence, Feedback, Psychological, Female, Humans, Learning, Male, Robotics, Robots, tutoring 2011
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(2011): Polite web-based intelligent tutors: Can they improve learning in classrooms?. In: Computers & Education 56 (3), S. 574-584. DOI: 10.1016/j.compedu.2010.09.019
DOI: https://doi.org/10.1016/j.compedu.2010.09.019 Abstract: Should an intelligent software tutor be polite, in an effort to motivate and cajole students to learn, or should it use more direct language? If it should be polite, under what conditions? In a series of studies in different contexts (e.g., lab versus classroom) with a variety of students (e.g., low prior knowledge versus high prior knowledge), the politeness effect was investigated in the context of web-based intelligent tutoring systems, software that runs on the Internet and employs artificial intelligence and learning science techniques to help students learn. The goal was to pinpoint the appropriate conditions for having the web-based tutors provide polite feedback and hints (e.g., ’Let’s convert the units of the first item’) versus direct feedback and hints (e.g., ’Convert the units of the first item now’). In the study presented in this paper, 132 high school students in a classroom setting, grouped as low and high prior knowledge learners according to a pre-intervention knowledge questionnaire, did not benefit more from polite feedback and hints than direct feedback and hints on either an immediate or delayed posttest, both of which contained near transfer and conceptual test items. Of particular interest and contrary to an earlier lab study, low prior knowledge students did not benefit more from using the polite version of a tutor. On the other hand, a politeness effect was observed for the students who made the most errors during the intervention, a different proxy for low prior knowledge, hinting that even in a classroom setting, politeness may be beneficial for more needy students. This article presents and discusses these results, as well as discussing the politeness effect more generally, its theoretical underpinnings, and future directions. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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