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VISUALIZATION IMPROVEMENT IN LEARNING ANALYTICS USING SEMANTIC ENRICHMENT

G. FERNÁNDEZ, O. MARIÑO

DISCOVERING INTERESTING PATTERNS IN AN E-LEARNING SYSTEM, 2016

Abstract

This chapter presents a learning analytics and knowledge representation framework to support analysis and visualization of data extracted from MOOC (Massive Open Online Course). Data analytics provides methods and tools to analyze big data sets. In the context of learning, these methods have helped analyze MOOC data, mainly to characterize groups of learners. On the other hand, semantic Web and eLearning research groups are working on ontological representation of learning scenario components. Our project joins those efforts, both to discover relations through bottom-up analytics and to organize and focus the analysis with a semantic representation of the learning scenario. The first section states the problem and sketches the solution. The proposed framework is composed of four stages: data cleaning and formatting, bottom-up data analysis, semantic analysis, and finally visualization. The specification and implementation of each step is described in the following sections. The last section presents the evaluation of the project and conclusions.