Hints vs Distractions in Intelligent Tutoring Systems: Looking for the proper type of help
Maria Blancas-Mu\~noz, Vasiliki Vouloutsi, Riccardo Zucca, Anna Mura,, Paul F.M.J. Verschure

TL;DR
This study investigates how different types of assistance from a robotic tutor, specifically hints versus distractions like jokes or facts, impact student performance in inquiry-based learning tasks.
Contribution
It compares the effects of hints and distractions, revealing that hints and curious facts improve learning more than humorous distractions in real-life tutoring scenarios.
Findings
Hints improve student performance.
Curious facts are more effective than humor as distractions.
Distractions with jokes are less beneficial.
Abstract
The kind of help a student receives during a task has been shown to play a significant role in their learning process. We designed an interaction scenario with a robotic tutor, in real-life settings based on an inquiry-based learning task. We aim to explore how learners' performance is affected by the various strategies of a robotic tutor. We explored two kinds of(presumable) help: hints (which were specific to the level or general to the task) or distractions (information not relevant to the task: either a joke or a curious fact). Our results suggest providing hints to the learner and distracting them with curious facts as more effective than distracting them with humour.
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Taxonomy
TopicsSocial Robot Interaction and HRI · AI in Service Interactions · Multimodal Machine Learning Applications
