HOW HAS THE LAK COMMUNITY EVOLVED IN ITS FIRST DECADE? A DETAILED MODELING THROUGH INTERACTIVE CNA SOCIOGRAMS

The process of identifying relevant scientific papers and authors is becoming more and more tedious both to neophytes, as well as to experts searching for state-of-the-art approaches, due to the high-speed development of recent emerging domains and the exponential increase of the available publications. This paper presents an overarching architecture grounded in Cohesion Network Analysis (CNA) and provides a detailed showcase on the papers published at the Learning Analytics & Knowledge (LAK) conference between 2011 and 2020. We applied our method to LAK because it is the most representative sub-community from the Learning Analytics domain. On the other hand, CNA Sociograms might be applied to journals, academic departments, business groups, etc., having the potential to aid in understanding the past and current foci of a community and its directions. We propose an end to end method, from crawling the ACM Digital Library to insights presentation, using our own Visual 2-Mode CNA Graph and visualizations in Kibana, customizable and interactive, that facilitates individuals’ interactions with a new domain. Our method brings valuable insights about the semantic relatedness between authors and other types of associations presented as links within the resulting 2-mode cohesion graph. Moreover, we introduce multiple statistics about top ranked authors, institutions, as well as the most central domain articles. In contrast to previous experiments, we introduce a comprehensive use case, an enhanced processing pipeline with new heuristics, as well as new CNA sociogram visualizations. This semantic-based approach facilitates inferring key authors’ and publications’ interrelations and trending topics over time.