Publications on Bodong Chen

Infrastructuring sustainable innovations in education: A quick update

Sun, 07 Apr 2024 00:00:00 +0000

I am excited about the emerging body of work on infrastructuring in the Learning Sciences and beyond. In this post I am sharing a few quick updates on this topic.

New JLS paper on infrastructuring

A paper titled A framework for infrastructuring sustainable innovations in education is published online in the Journal of the Learning Sciences. This paper was inspired by many and has been in the works since 2022. I have received amazingly thoughtful feedback from anonymous JLS reviewers, as well as several colleagues I collaborate with on this topic.

A framework for infrastructuring sustainable innovations in education

Wed, 27 Mar 2024 00:00:00 +0000

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. Infrastructuring can be defined as the ongoing process of creating functional infrastructures for activities in a particular context. I describe six inter-connected areas of infrastructuring to highlight ways that learning designers and researchers could surface existing infrastructures, facilitate coherence-making negotiation activities, engage practitioners in collaborative design, and continuously evaluate infrastructuring activities. It is hoped that such advances will increase the relevance and sustainability of innovations in education and provide infrastructure to further improve them.

Integrating generative AI in knowledge building

Sat, 11 Nov 2023 00:00:00 +0000

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. Following the teacher’s pedagogical design, students used ChatGPT for a range of distinct purposes. Student interviews showed detailed processes of using ChatGPT for knowledge building and students’ emerging AI literacy in multiple dimensions. This study offers practical implications for the integration of GenAI in K-12 education and urges educators to create spaces and scaffolds for students to mindfully engage with GenAI in the classroom.

Learning Analytics for Understanding and Supporting Collaboration

Mon, 21 Mar 2022 00:00:00 +0000

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). Grounded in the conceptual exploration, we then distinguish two salient strands of collaboration analytics: (a) computational analysis of collaboration that applies computational methods to examining collaborative processes; and (b) analytics for collaboration which is primarily concerned with designing and deploying data analytics in authentic contexts to facilitate collaboration. Examples and cases representing different contexts for learning and analytical frames are presented, followed by a discussion of key challenges and future directions.

Socio-Semantic Network Motifs Framework for Discourse Analysis

Sun, 13 Mar 2022 00:00:00 +0000

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. While more work needs to be done, the SSN motifs framework shows promise as a novel, theoretically informed approach to discourse analysis.

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

Fri, 11 Mar 2022 00:00:00 +0000

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. Careful work must meaningfully situate network methods and interventions within the theoretical assumptions explaining learning, as well as within pedagogical and technological factors shaping learning processes. The five empirical papers in the special section demonstrate diverse applications of network analysis, and the invited commentaries from cognitive network science and physics education research further discuss potential synergies between learning analytics and other sister fields with a shared interest in leveraging network science. We conclude by discussing opportunities to strengthen the rigour of network-based learning analytics projects, expand current work into nascent areas, and achieve more impact by holistically addressing the full cycle of learning analytics.