Learning Strategies

Introduction of learners: The purpose of this evaluation is to evaluate the effectiveness of text and printing, especially the fluency and use of languages, recognition of lighting style and function, the ability of decoding and encoding, the level of spelling, It is to explain. E, 4th grader of 9 years old, I attend a local elementary school in St. Andrew's School of Drexel Hill. Evaluation is done with one-to-one guidance on various dates. She is a cooperative student, but she is not a risk taker.

Rubin (1975) defines the learning strategy as "technology or equipment that learners can use to acquire knowledge". She divides these "technologies and devices" directly into learning strategies and indirect learning strategies. The direct strategy is directly related to the project or problem being investigated. They are clarification / validation, monitoring, memory, speculation / induction reasoning, deduction inference and practice. Indirect learning strategy focuses on learning environment adaptation: creation

The neural network can use one of three learning strategies: supervised learning strategy, unsupervised learning strategy, or intensive learning strategy. Supervised learning requires at least two data sets, a training set of inputs with expected outputs, and a test set of inputs with no expected output. These two sets of data must consist of tag data, ie the target's data pattern is known in advance. Unsupervised learning strategy is often used to find hidden structures in unlabeled data (such as hidden Markov chains). They behave just like the clustering algorithm. The foundation of reinforcement learning is a simple premise to reward a neural network to punish good behavior and bad behavior. Since unsupervised learning enhancement strategy does not need to tag data, it can be applied to unresolved problems with unknown correct output.