Name
Title | ||
|---|---|---|
Do micro-level tutorial decisions matter: applying reinforcement learning to induce pedagogical tutorial tactics |
Abstract | ||
|---|---|---|
Pedagogical tutorial tactics are policies for a tutor to decide the next action when there are multiple actions available. When the contents were controlled so as to be the same, little evidence has shown that tutorial decisions would impact students' learning. In this paper, we applied Reinforcement Learning (RL) to induce two sets of tutorial tactics from pre-existing human interaction data. The NormGain set was derived with the goal of enhancing tutorial decisions that contribute to learning while the InvNormGain set was derived with the goal of enhancing those decisions that contribute less or even nothing to learning. The two sets were then compared with human students. Our results showed that when the contents were controlled so as to be the same, different pedagogical tutorial tactics would make a difference in learning and more specifically, the NormGain students outperformed their peers. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13388-6_27 | Intelligent Tutoring Systems (1) |
Keywords | Field | DocType |
human student,micro-level tutorial decisions matter,normgain student,invnormgain set,pedagogical tutorial tactic,normgain set,pre-existing human interaction data,tutorial decision,tutorial tactic,reinforcement learning,different pedagogical tutorial tactic,artificial intelligence,human interaction | TUTOR,Computer science,Knowledge management,Human interaction,Human learning,Artificial intelligence,Error-driven learning,Machine learning,Reinforcement learning | Conference |
Volume | ISSN | ISBN |
6094 | 0302-9743 | 3-642-13387-8 |
Citations | PageRank | References |
33 | 1.29 | 38 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Chi | 1 | 48 | 7.00 |
| Kurt VanLehn | 2 | 2352 | 417.44 |
| Diane J. Litman | 3 | 3542 | 484.90 |