Embedding Simple Additive Weight (SAW) Technique as a Means of Deciding a Cluster Element in An Adaptive Learning System

Source abstract

Simple Additive Weight (SAW) is a technique within the Multi-Attribute Decision Making (MADM) that can be used in determining the level of accuracy in decision result that involves multiple attributes. It is based on identifying the benefits and non-benefits of attributes through a decision making process. In this work, the SAW technique is used to extract and evaluate learner’s decision from returned SQL statements. The retrieved data contains responses of learners in respect to learning style preferences. In order to accurately place each learner in the right cluster of learning pedagogy, SAW is used to determine the benefits and non-benefits of each response, the result for each learner is summed up, which is used as a comparison variable for accurate placement into existing defined learning clusters.