Analysis and Identification of Data Heterogeneity on Learning Environment Using Ontology Knowledge

Heterogeneity on learning environment is about different data and applications to support a learning process in education institutions. Distributed and various systems on learning environment is the current issues to produce big and heterogeneity data problem. A lot of relationships are formed between elements on learning environment. The element on learning environment consists of learning data, learning applications, data sources, learning concept, and data heterogeneity aspect on learning environment. These elements are interrelated and produce complex relationship between each other. A complex relationship problem between elements on learning environment makes a process of analysis and identification difficult to be done. Existing method to drawing this heterogeneity problem make confuse and misunderstanding readers. To solved this problem, researcher using ontology knowledge to describe and draw a semantic relationship that represent the complexity of data relationship on learning environment. The result of this analysis is to develop ontology knowledge to solve complexity relationship on learning environment, and also to help reader\u27s better understanding the complex relationship between elements on learning environment

Computational Models of Learner’s Interaction in Collaborative Learning

Current efforts on Computer Support Collaborative Learning (CSCL) include the design of computational models of collaborative learning interaction such as to improve support guidance to human participation. However, during collaborative learning activities, the interaction process among learners is too complex and it is difficult for teachers or designers to analyze and measure learning effects. Computational models of collaborative learning interaction have been known as a method that provide functional computer-based representations to help educators understand, explain, and identify patterns of group behavior and hence support group learning processes. The aim of this paper is to give an overview on three computational models of learner’s interaction and support possibilities afforded by the various types of computational models of collaborative learning processes. This paper demonstrates how these models can be used to study the nature of interaction patterns within collaborative learning in e-learnin