A multi-strategy machine learning student modeling for intelligent tutoring systems: Based on blackboard approach

Research output: Contribution to journalArticle

9 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)274-293
Number of pages20
JournalLibrary Hi Tech
Volume31
Issue number2
DOIs
Publication statusPublished - 2013 Jul 15

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Intelligent systems
Expert systems
Learning systems
Students
learning
student
Case based reasoning
learning process
Teaching
methodology
Values

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Library and Information Sciences

Cite this

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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.",
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A multi-strategy machine learning student modeling for intelligent tutoring systems : Based on blackboard approach. / Huang, Mu Jung; Chiang, Heien Kun; Wu, Pei Fen; Hsieh, Yu Jung.

In: Library Hi Tech, Vol. 31, No. 2, 15.07.2013, p. 274-293.

Research output: Contribution to journalArticle

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AU - Chiang, Heien Kun

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AU - Hsieh, Yu Jung

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