DocumentCode :
3170044
Title :
Self-Directed Learner
Author :
Ni, Eileen A. ; Ling, Charles X.
Author_Institution :
Dept. of Comput. Sci., Univ. of Western Ontario, London, ON, Canada
fYear :
2010
fDate :
29-30 Oct. 2010
Firstpage :
410
Lastpage :
413
Abstract :
Traditional supervised learning algorithms choose labeled training examples in a given sequence passively. However, in many real-world situations, a learner can choose which training example to learn, and its goal is to minimize the number of mistakes that the learner currently predicts for such training examples. In this paper, we propose a simple yet effective human-oriented supervised learning paradigm, Self-Directed Learner (SDL), which explicitly exploits a human learning strategy to solve this problem. SDL chooses the example that is predicted with the most certain label to learn and updates its model gradually. We conduct the experiments on a well-known educational software with both our learning algorithm and human beings. The experiment results show that HOL is able to minimize the number of mistakes efficiently. In addition, it models the human learning process much better than other learning algorithms.
Keywords :
computer aided instruction; learning (artificial intelligence); user interfaces; educational software; human-oriented supervised learning; self-directed learner; human learning; minimal number of mistakes; supervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence and Education (ICAIE), 2010 International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-6935-2
Type :
conf
DOI :
10.1109/ICAIE.2010.5641154
Filename :
5641154
Link To Document :
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