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• W2975170551 abstract "In this paper, we propose that the data generated by educational robots can be better used by applying learning analytics methods and techniques which can lead to a deeper understanding of the learners' apprehension and behavior as well as refined guidelines for roboticists and improved interventions by the teachers. As a step towards this, we put forward analyzing behavior and task performance at team and/or individual levels by coupling robot data with the data from conventional methods of assessment through quizzes. Classifying learners/teams in the behavioral feature space with respect to the task performance gives insight into the behavior patterns relevant for high performance, which could be backed by feature ranking. As a use case, we present an open-ended learning activity using tangible haptic-enabled Cellulo robots in a classroom-level setting. The pilot study, spanning over approximately an hour, is conducted with 25 children in teams of two that are aged between 11-12. A linear separation is observed between the high and low performing teams where two of the behavioral features, namely number of distinct attempts and the visits to the destination, are found to be important. Although the pilot study in its current form has limitations, e.g. its low sample size, it contributes to highlighting the potential of the use of learning analytics in educational robotics." @default.
• W2975170551 created "2019-10-03" @default.
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• W2975170551 date "2019-10-01" @default.
• W2975170551 modified "2023-10-14" @default.
• W2975170551 title "Robot Analytics: What Do Human-Robot Interaction Traces Tell Us About Learning?" @default.
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• W2975170551 doi "https://doi.org/10.1109/ro-man46459.2019.8956465" @default.
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