In this paper, we describe the development of a mobile butterfly-watching learning (BWL)
system to realize outdoor independent learning for mobile learners. The mobile butterfly-watching
learning system was designed in a wireless mobile ad-hoc learning environment. This is first result
to provide a cognitive tool with supporting the independent learning by applying PDA with
wireless communication technology to extend learning outside of the classroom. Independent
learning consists of self-selection, self-determination, self-modification, and self-checking.
Our approach to understanding mobile learning begins by describing a dialectical
approach to the development and presentation of a task model using the sociocognitive
engineering design method. This analysis synthesises relevant theoretical
approaches. We then examine two field studies which feed into the development of
the task model.
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
