Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Going Beyond the Problem Given: How Human Tutors Use Post-Solution Discussions to Support Transfer
International Journal of Artificial Intelligence in Education - "Caring for the Learner" in honour of John Self
Learning factors analysis – a general method for cognitive model evaluation and improvement
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Leveraging hidden dialogue state to select tutorial moves
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
User Modeling and User-Adapted Interaction
Characterizing the effectiveness of tutorial dialogue with hidden markov models
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Question ranking and selection in tutorial dialogues
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Hi-index | 0.00 |
While high interactivity has been one of the main characteristics of one-on-one human tutoring, a great deal of controversy surrounds the issue of whether interactivity is indeed the key feature of tutorial dialogue that impacts students' learning results. There are two commonly held hypotheses regarding the issue: a widely-believed monotonic interactivity hypothesis and a better supported interaction plateau hypothesis. The former hypothesis predicts increasing in interactivity causes an increase in learning while the latter states that increasing interactivity yields increasing learning until it hits a plateau, and further increases in interactivity do not cause noticeably increase in learning. In this study, we proposed the tactical interaction hypothesis which predicts beyond a certain level of interactivity, further increases in interactivity do not cause increase in learning unless they are guided by effective tutorial tactics. Overall our results support this hypothesis. However, finding effective tactics is not easy. This paper sheds some light on how to apply Reinforcement Learning to derive effective tutorial tactics.