Increasing and ease of access to big data have led to new developments in e-Learning and increased attention and research in the field of learning analytics. The purpose of this research is to introduce The Social Network Analysis (SNA) method as one of the new applications of learning analytics for detecting learning networks in e-Learning environments. This study discusses the importance of social network analysis and its definition, then introduces its applications in different domains, followed by analytical methods and evaluation criteria in social network analysis. The research results show that because of the achievements of multi-disciplines like mathematics, sociology, computer science and education, social network analysis is an interdisciplinary and high-performance domain for studying and demonstrating communication patterns that have never been easier to use. For this reason, it requires communities of professionals with different disciplines.

DOCUMENT CLUSTERING IS A USEFUL TECHNIQUE TO ORGANIZE LARGE SETS OF DOCUMENTS INTO MEANINGFUL GROUPS. THE USEFULNESS IS APPRECIATED BY LABELING THE CLUSTERS WITH RELEVANT WORDS THAT DESCRIBE THEIR ASSOCIATED DOCUMENTS. THE TRADITIONAL APPROACH FOR DOCUMENT CLUSTERING, I.E. BAG-OF-WORDS REPRESENTATION, OFTEN IGNORES THE SEMANTIC RELATIONS BETWEEN TERMS. HENCE, ONTOLOGY-BASED DOCUMENT CLUSTERING IS PROPOSED. IN THE CONTEXT OF e-Learning, THE RICHER ANNOTATION OF LEARNING MATERIALS, VIA THE USE OF APPROPRIATE ONTOLOGIES IS A WAY TO DEAL WITH THE REUSABILITY AND REMIX OF LEARNING OBJECTS. THROUGH PROVIDING A SEMANTIC INFRASTRUCTURE THAT WILL EXPLICITLY DECLARE THE SEMANTICS AND RELATIONS BETWEEN CONCEPTS USED IN LABELING LEARNING OBJECTS, THE DESIRED QUALITY IN THE LEARNING OFFER CAN BE ENSURED. THIS PAPER PROPOSES AN ONTOLOGY-BASED DOCUMENT CLUSTERING APPROACH BASED ON TWO-STEP CLUSTERING ALGORITHM AND COMPARES ITS PERFORMANCE WITH THE CONVENTIONAL CLUSTERING. ONTOLOGY IS INTRODUCED THROUGH DEFINING A WEIGHTING SCHEME THAT INTEGRATES TRADITIONAL SCHEME, I.E. CO-OCCURRENCES OF WORDS, WITH WEIGHTS OF RELATIONS BETWEEN WORDS IN ONTOLOGY. OUR EXPERIMENTAL EVALUATIONS ARE PERFORMED ON ICVL (INTERNATIONAL CONFERENCE ON VIRTUAL LEARNING) PAPER COLLECTION AS DATASET WITH e-Learning DOMAIN ONTOLOGY AS THE BACKGROUND KNOWLEDGE. THE ONTOLOGY WAS IMPLEMENTED BY US THROUGH A DIFFERENT RESEARCH. THE RESULTS SHOW THAT INCLUSION OF ONTOLOGY INCREASES THE CLUSTERING QUALITY.