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Full-Text Articles in Education
Designing For Deep And Meaningful Student-To-Content Interactions, Joanna Dunlap, Donna Sobel, Deanna Sands
Designing For Deep And Meaningful Student-To-Content Interactions, Joanna Dunlap, Donna Sobel, Deanna Sands
Joanna Dunlap
Online education has skyrocketed in popularity. Every year, more universities are starting online programs. This increase is mostly due to institutional economics, and the demands of students who face a number of obstacles that make the on-campus format inconvenient. The University of Colorado at Denver and Health Sciences Center is no different. Over the last few years, there have been numerous institutional initiatives to encourage faculty to create new online programs or online versions of existing on-campus programs. As part of a program level effort to offer a fully online licensure program in the area of special education that would …
Cognitive Apprenticeships: An Instructional Design Review, Brent Wilson, Peggy Cole
Cognitive Apprenticeships: An Instructional Design Review, Brent Wilson, Peggy Cole
Brent Wilson
This discussion of the relationship between two related disciplines--cognitive psychology and instructional design (ID)--characterizes instructional design as a more applied discipline, which concerns itself more with prescriptions and models for designing instruction, while instructional psychologists conduct empirical research on learning and instructional processes. It is posited that a problem-solving orientation to education is needed if schoo]s are to achieve substantial learning outcomes, and the concept of cognitive apprenticeships, which emphasize returning instruction to settings where worthwhile problems can be worked with and solved, is proposed as a possible solution to this problem. A brief review of ID models focuses on …
Maximum-Entropy Estimated Distribution Model For Classification Problems, L Tan, D Taniar
Maximum-Entropy Estimated Distribution Model For Classification Problems, L Tan, D Taniar
Dr Ling Tan
Classification is a fundamental problem in machine learning and data mining. This paper applies a stochastic optimization model to classification problems. The proposed maximum entropy estimated distribution model uses a probabilistic distribution to represent solution space, and a sampling technique to explore search space. This paper demonstrates the application of the proposed maximum entropy estimated distribution model to improve linear discriminant function and rule induction methods. In addition, this paper compares the proposed classification model with decision trees. It shows that the proposed model is preferable to decision tree C4.5 in the following cases: i) when prior distribution of classification …