Deriving group profiles from social media to facilitate the design of simulated environments for learning

Abstract

Simulated environments for learning are becoming increasingly popular to support experiential learning in complex domains. A key challenge when designing simulated learning environments is how to align the experience in the simulated world with real world experiences. Social media resources provide user-generated content that is rich in digital traces of real world experiences. People comments, tweets, and blog posts in social spaces can reveal interesting aspects of real world situations or can show what particular group of users is interested in or aware of. This paper examines a systematic way to analyze user-generated content in social media resources to provide useful information for learning simulator design. A hybrid framework exploiting Machine Learning and Semantics for social group profiling is presented. The framework has five stages: (1) Retrieval of user-generated content from the social resource (2) Content noise filtration, removing spam, abuse, and content irrelevant to the learning domain; (3) Deriving individual social profiles for the content authors; (4) Clustering of individuals into groups of similar authors; and (5) Deriving group profiles, where interesting concepts suitable for the use in simulated learning systems are extracted from the aggregated content authored by each group. The framework is applied to derive group profiles by mining user comments on YouTube videos. The application is evaluated in an experimental study within the context of learning interpersonal skills in job interviews. The paper discusses how the YouTube-based group profiles can be used to facilitate the design of a job interview skills learning simulator, considering: (1) identifying learning needs based on digital traces of real world experiences; and (2) augmenting learner models in simulators based on group characteristics derived from social media.