A Multimodal Learning Analytics Approach to Support Evidence-based Teaching and Learning Practices

Abstract

Multimodal Learning Analytics (MMLA) aims to support evidence-based educational practices by collecting, processing, analyzing and sense-making of multimodal evidence of learning. MMLA is not widespread yet and there are few tailored MMLA solutions to meet the requirements of a specific learning scenario. This PhD project investigates the main challenges behind the limited development of MMLA solutions and proposes an MMLA infrastructure as the main contribution. The proposed infrastructure includes three components - a data value chain, a data model and a software architecture. The overall project follows the design-based research methodology where multiple iterations are involved to refine the contributions.