Paper Info
Title
A multi-strategy machine learning student modeling for intelligent tutoring systems: Based on blackboard approach.
Abstract
Purpose - This study aims to propose a blackboard approach using multistrategy machine learning student modeling techniques to learn the properties of students' inconsistent behaviors during their learning process. Design/methodology/approach - These multistrategy machine learning student modeling techniques include inductive reasoning (similarity-based learning), deductive reasoning (explanation-based learning), and analogical reasoning (case-based reasoning). Findings - According to the properties of students' inconsistent behaviors, the ITS (intelligent tutoring system) may then adopt appropriate methods, such as intensifying teaching and practicing, to prevent their inconsistent behaviors from reoccurring. Originality/value - This research sets the learning object on a single student. After the inferences are accumulated from a group of students, what kinds of students tend to have inconsistent behaviors or under what conditions the behaviors happened for most students can be learned.
Year
DOI
Venue
2013
10.1108/07378831311329059
LIBRARY HI TECH
Keywords
DocType
Volume
Student modelling,Similarity-based learning,Explanation-based learning,Case-based reasoning,Intelligent tutoring system,Learning,Students,Individual behaviour
Journal
31.0
Issue
ISSN
Citations 
2.0
0737-8831
0
PageRank 
References 
Authors
0.34
12
4
Name
Order
Citations
PageRank
Mu-Jung Huang121811.90
Heien-Kun Chiang2215.14
Pei-Fen Wu381.50
Yu-Jung Hsieh400.34