LAUSR

Nowadays, the use of technology in continuously increasing, making a significant impact in almost every area, including education. New areas have gained much popularity in the last years in educational technology (EdTech), such as Massive Open Online Courses (MOOCs) or computer-supported collaborative learning. In addition, research and interest in this area have also been growing over the years. The quantity of research and scientific publications in EdTech is constantly increasing, and trying to analyze and extract information from a set of research papers is often a very time-consuming task. To make this process easier and solve these limitations, we present Fontana, a framework that can quickly perform trend and social network analysis using any corpus of documents and its metadata. Specifically, the framework can: 1) Discover the latest trends given any corpus of documents, using Natural Language Processing (NLP) analysis and keywords (bibliometric approach); 2) Discover the evolution of the trends previously identified over the years; 3) Discover the primary authors and papers, along with hidden relationships between existing communities. To test its functionality, we evaluated the framework using a corpus of papers from the EdTech research field. We also followed an open science methodology making the entire framework available in Open Science Framework (OSF) easy to access and use. The case study successfully proved the capabilities of the framework, revealing some of the most frequent topics in the area, such as “EDM,” “learning analytics,” or “collaborative learning.” We expect our work to help identifying trends and patterns in the EdTech area, using natural language processing and social network analysis to objectively process large amounts of research.