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.
This study investigates the incorporation of peer-to-peer (P2P) computing model into smartphone, and discusses its potential benefits for collaborative mobile users. The goal of this study is to establish a P2P computing environment for network-capable smartphone. Besides, in order to support the interoperability of smartphones and allow them to share information seamlessly, this study proposes a service oriented P2P framework, utilizing XML and SOAP as the underlying communication media. A prototyping system of the proposed system is implemented. Its implications and applications are also discussed.
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