Hints vs Distractions in Intelligent Tutoring Systems: Looking for the proper type of help
This work addresses the design of intelligent tutoring systems for education, but it is incremental as it compares specific help strategies without major methodological breakthroughs.
The study investigated how different types of help from a robotic tutor affect learner performance in an inquiry-based task, finding that hints and curious facts were more effective than humor.
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.