Defining the Boundaries Between Artificial Intelligence in Education, Computer-Supported Collaborative Learning, Educational Data Mining, and Learning Analytics: A Need for Coherence
Bart Rienties, Henrik Køhler Simonsen, Christothea Herodotou
Abstract
This review aims to define the boundaries of four distinct research fields of Artificial Intelligence and EDucation (AIED), Computer-Supported Collaborative Learning (CSCL), Educational Data Mining (EDM), and Learning Analytics (LA). While all four fields are focused on understanding learning and teaching using technology, each field has a relatively unique or common perspective on which theoretical frameworks, methods, and ontologies might be appropriate. In this review we argue that researchers should be encouraged to cross the boundaries of their respective field and work together to address the complex challenges in education.
Topics & Concepts
Learning analyticsEducational data miningAnalyticsComputer scienceData scienceCoherence (philosophical gambling strategy)Collaborative learningBusiness intelligenceKnowledge managementPhysicsQuantum mechanicsOnline Learning and AnalyticsIntelligent Tutoring Systems and Adaptive LearningE-Learning and Knowledge Management