Research in machine learning often focuses either on inductive learning learning from experience with minimal reliance on prior theory or, more recently, on explanation-based learning deducing general descriptions from theories with minimal reliance on experience. Theory revision unites these two concerns: one must revise one’s theory in the light of experience, but one must simultaneously use ...

This paper combines a critique of the underlying theories of concept mapping for university level learning and case study data from two students, one in Classics and one in Neuroscience. A shift from the cognitive perspectives of assimilation learning theory towards a more narrative and representational visualisation is described and used to foreground a more dialogic approach. The results and ...

Early machine learning research was strongly interrelated with research on human category learning while later on the focus shifted to the development of algorithms with high performance. Only recently, there is a renewed interest in cognitive aspects of learning. Machine learning approaches might be able to model and explain human category learning while cognitive models might inspire new, mor...

Although artificial intelligence has been successfully introduced to enhance Education through technologies in the past few years, major challenges still remain. One of them is how to represent the knowledge of intelligent systems. To represent the knowledge of systems to support collaborative learning is particularly challenging because it is based on various learning theories and given the co...

In psychology many of theories of skill acquisition have had great success in addressing the fine details of learning relatively simple tasks, but can they scale up to complex tasks that are more typical of human learning in the real world? In this paper we describe production composition, a theory of skill acquisition that combines aspects of the theories forwarded by Anderson (1982) and Newel...

Several theories of learning have been proposed to account for the acquisition of abstract, generative knowledge including schema theory, analogical learning and implicit learning. However, past research has not compared these three theories directly. In the present studies we instantiated each theory as a learning scenario (i.e., direct instruction, analogy training and implicit training) and ...

Although the Dunns’ learning styles and Gardner’s multiple intelligences theories are two distinct areas of research, they do not oppose each other and can be used together to improve learning. The design of this tutorial is an attempt to integrate students’ learning styles and their unique blend of intelligences in the library research process. The goal is to encourage higher-order thinking so...

This paper presents a complete inductive learning system that aims to produce comprehensible theories for XML document classifications. The knowledge representation method is based on a higherorder logic formalism which is particularly suitable for structured-data learning systems. A systematic way of generating predicates is also given. The learning algorithm of the system is a modified standa...

Advances in electronic technologies offer e-learning applications the possibility to become skillful and acquire knowledge on a task. The purpose of this paper is twofold. First to review papers related to usability methods and learning theories currently used to design e-learning applications. Secondly, to draw on the author’s experience from carrying out a test with a web-based testing applic...

Situated learning theories are largely based on the idea that the knower cannot be separated from the known, i.e. the individual, its context, and its activity in the environment mutually constitute each other. This way of attending the issues of cognition and learning has been heavily criticised by many researchers since sociocultural aspects such as social interaction and tool use are not bel...