Instance-Based Learning Online

With concepts and information pouring it seems overwhelming to achieve learning both from the student’s point of view and also from the teacher’s point of view. Then you need to classify chunks of knowledge and to do this you need to do away with generalizations for some time. This means that you need to shift your focus from the number of principles to the nature of principles. If you have to teach quadratic equations you can do well to explain the nature of the types of equations that a learner would come across. This makes learning on the whole instance-specific and not quantity-specific. In online tutoring instance-based learning is given due importance. It has been felt necessary to compare the nature of new problems with problems solved in your tutoring sessions to illustrate the principle or concept. By understanding the difference between problems the learner will be able to classify problems and acquire appropriate thinking skills. As a lesson is based fundamentally on thought the learner will understand different ways of thinking that different problems demand. When do we use the basic equations formula and how do we break down a problem? By asking such questions the learner delves into the nature of the problem and is then in a position to compare what he/she has learnt with the problem that demands application. Although instance-based learning is called lazy learning it teaches learners to construct and hypothesize directly from the problems and concepts explained in the tutoring sessions. This may seem like a short cut but in reality it teaches the nature of the types of problems that one comes across in class-work, homework, assignments and exams. We would also need to recall what we have studied in our classes and so this itself is a kind of comparative activity which sharpens memory and thinking skills. It must also be noted that application of rules gets complicated with newer problems. In this sense instance-based learning proves fruitful because it gives learners flexible models that can be adapted to cracking different types of problems. Some riders in Geometry may need you to apply the slide rule to solve them while for some you may need to invoke the Pythagoras theorem. A rigid model of learning suffers from instability as when one model of thinking is no longer useful it is disposed off. Whereas in instance-based learning the hypothesis remains but only the old instance gets replaced.