A framework for infrastructuring sustainable innovations in education

Abstract Learning scientists have historically been interested in understanding how learning happens and in creating innovations to facilitate learning in real-world situations. Recently, the field has recognized that advancing standalone innovations is not enough to address systemic problems in education; instead, the focus must be broadened to sustain these innovations. Drawing on an interdisciplinary body of literature on infrastructure, this paper presents a framework—the IMPROV framework—that offers theoretical, methodological, and practical tools for infrastructuring innovations in the learning sciences.

Integrating generative AI in knowledge building

Abstract Generative artificial intelligence (GenAI) is penetrating in various social sectors, motivating a strong need for teaching AI literacy in younger generations. While substantial efforts have been made to teach AI literacy and to use AI to facilitate learning, few studies have provided empirical accounts of students’ nuanced processes of using GenAI for learning. In this study, we engaged a group of high school students in leveraging ChatGPT to support their knowledge building efforts.

Learning Analytics for Understanding and Supporting Collaboration

Abstract Collaboration is an important competency in the modern society. To harness the intersection of learning, work, and collaboration with analytics, several fundamental challenges need to be addressed. This chapter about collaboration analytics aims to highlight these challenges for the learning analytics community. We first survey the conceptual landscape of collaboration and learning with a focus on the computer-supported collaborative learning (CSCL) literature while attending to perspectives from computer supported cooperative work (CSCW).

Socio-Semantic Network Motifs Framework for Discourse Analysis

ABSTRACT Effective collaborative discourse requires both cognitive and social engagement of students. To investigate complex socio-cognitive dynamics in collaborative discourse, this paper proposes to model collaborative discourse as a socio-semantic network (SSN) and then use network motifs – defined as recurring, significant subgraphs – to characterize the network and hence the discourse. To demonstrate the utility of our SSN motifs framework, we applied it to a sample dataset.

Networks in Learning Analytics: Where Theory, Methodology, and Practice Intersect

Keywords: network analysis, networked learning, social network analysis, learning analytics, network science, editorial ABSTRACT Network analysis has contributed to the emergence of learning analytics. In this editorial, we briefly introduce network science as a field and situate it within learning analytics. Drawing on the Learning Analytics Cycle, we highlight that effective application of network science methods in learning analytics involves critical considerations of learning processes, data, methods and metrics, and interventions, as well as ethics and value systems surrounding these areas.