A Framework for Normalization of Homogeneous and Semi-homogeneous E-Lessons

An e-lesson can be of Homogeneous or Semi-homogeneous nature. Even if a subset of such e-lessons has only one module (m1) in common with other subsets, the “common module” may not contain the same array of concepts. As a result, any investigation of such e-lessons based solely on their module names is inherently flawed. Normalization of e-lessons can remove such a flaw. A set of N Homogeneous and Semi-homogenous e-lessons are considered normalized if same-name modules cover the same concepts. In this paper a framework for normalization of e-lessons is presented as the first phase of creating a mechanism for accurate e-lesson modules´ cross-referencing, indexing, retrieving, and data mining.