Coding schemes as lenses on collaborative learning
| DOI | https://doi.org/10.1108/ILS-08-2019-0079 |
| Date | 12 December 2019 |
| Published date | 12 December 2019 |
| Pages | 1-18 |
| Author | Yuxin Chen,Christopher D. Andrews,Cindy E. Hmelo-Silver,Cynthia D'Angelo |
| Subject Matter | Library & information science,Librarianship/library management,Library & information services |
Coding schemes as lenses on
collaborative learning
Yuxin Chen and Christopher D. Andrews
Department of Learning Sciences, Indiana University Bloomington School of
Education, Bloomington, Indiana, USA
Cindy E. Hmelo-Silver
Center for Research on Learning and Technology, Indiana University Bloomington
School of Education, Bloomington, Indiana, USA, and
Cynthia D’Angelo
Department of Educational Psychology,
University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
Abstract
Purpose –Computer-supportedcollaborative learning (CSCL) is widely used in different levelsof education
across disciplines and domains. Researchers in the field have proposed various conceptual frameworks
toward a comprehensive understanding of CSCL. However, as the definition of CSCL is varied and
contextualized,it is critical to develop a sharedunderstanding of collaboration and commondefinitions for the
metricsthat are used. The purpose of this research is to present a synthesis that focusesexplicitly on the types
and featuresof coding schemes that are used as analytictools for CSCL.
Design/methodology/approach –This research collected coding schemes from researchers with
diverse backgrounds who participated in a series of workshops on collaborative learning and adaptive
support in CSCL, as well as coding schemes fromrecent volumes of the International Journal of Computer-
Supported Collaborative learning (ijCSCL). Each original coding scheme was reviewed to generate an
empiricallygrounded framework that reflects collaborativelearning models.
Findings –The analysis generated 13 categories, which were further classified into three domains: cognitive,
social and integrated. Most coding schemes contained categories in the cognitive and integrated domains.
Practical implications –This synthesized coding schemecould be used as a toolkit for researchers to
pay attention to the multiple and complex dimensionsof collaborative learning and for developing a shared
languageof collaborativelearning.
Originality/value –By analyzing a set of coding schemes,the authors highlight what CSCL researchers
find important by making theseimplicit understandings of collaborative learning visibleand by proposing a
common languagefor researchers across disciplines to communicate by referencinga synthesized framework.
Keywords Content analysis, Synthesis, Collaboration, CSCL, Coding schemes,
Technology-rich learning environments
Paper type Research paper
Introduction
To increase the impact of computer-supported collaborative learning (CSCL) research,
make it accessible to a broader audience and support further synthesis efforts, it is
critical to develop a shared understanding of collaboration and common definitions for
the metrics that are used for studying CSCL phenomena. Technology can play an
important role in collaborative learning but appropriate analysis of its role in
collaborative learning requires having common ways of understanding what researchers
Coding
schemes as
lenses
1
Received3 August 2019
Revised4 November 2019
Accepted12 November 2019
Informationand Learning
Sciences
Vol.121 No. 1/2, 2020
pp. 1-18
© Emerald Publishing Limited
2398-5348
DOI 10.1108/ILS-08-2019-0079
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2398-5348.htm
mean by collaboration. The CSCL field has made important interdisciplinary
contributions in this regard but definitions of high-quality collaboration still often remain
implicit. The use of CSCL in various learning contexts has been demonstrated to have
moderate effect sizes on a range of learning outcomes, processes, and affective variables
(Chen et al., 2018;Jeong et al., 2019). A defining feature of CSCL research is the study of
collaborative learning to illuminate underlying mechanisms and processes rather than
treating collaborative learning as a black box (Dillenbourg, 1999). Given the diverse
approaches to collaborative learning in the CSCL community, it is valuable to reflect on
current research and synthesize different approaches to analyze the similarities and
differences in collaborative processes (O’Donnell and Hmelo-Silver, 2013;Stahl et al.,
2014). In addition, investigating how researchers across disciplines (e.g. learning
sciences, computer sciences, educational measurement) define the dimensions of CSCL
can help to establish a common language across communities and improve
communication, identify potential gaps in understanding, promote sharing of similar
analytic tools and support synthesis efforts in CSCL (Jeong et al., 2014). To make this
research accessible to a broader audience, this paper presents a research synthesis that
focuses explicitly on the types and features of coding schemes that are used as analytic
tools for CSCL.
Some researchers in the fieldhave used a meta-analytic approach to investigate different
aspects of CSCL. Jeong et al. (2019) analyzed outcomes from 143 studies published between
2005 and 2014 and conducted a statistical meta-analysis to examine the effects of CSCL in
science, technology, engineering and mathematics (STEM) education. They found that the
overall effect size of CSCL interventions in STEM was 0.51and was moderated by different
variables, such as learner level, disciplines, and types of technology. Chen et al. (2018)
analyzed 425 studies to explore the effects of CSCL according to three main elements: the
role of collaboration, the use of computers and the use of extra learning tools or strategies.
These meta-analyticstudies of CSCL used quantitative methods and reported the effect sizes
of the design and implementationof empirical CSCL studies. They did not, however, include
how the researchers from those studies defined CSCL in their data. In contrast, our study
used a qualitative approach to look at the dimensions of CSCL by analyzing the coding
schemes used in CSCL research and, coding schemes, by their very nature, present the
definitions taken up by the researchers.
One way to understand what CSCL researchers view as important in collaborative
learning is to examine their coding schemes. Thisis particularly important given the range
of theoretical perspectives that are used in the field (Jeong et al.,2014). Situated in different
models of collaborative learning,coding schemes provide a unique point of view to compare
how scholars have examined students’actions and interaction in collaboration. Coding
schemes help researchers to identify common themes within their settings and recognize
specific dimensions that support or inhibit collaborative learning, such as a cognitive skill
required by a specific discipline or by a specific age group. As such, these coding schemes
represent the core indicators of collaboration from the perspective of CSCL researchers.
Although individual researcherscan focus on what is occurring within particular settings, a
synthesis helps identifythese elements across settings.
The data for this study come primarily from a series of workshops funded by the US
National Science Foundation (NSF) to build interdisciplinary capacity for understanding
and supporting CSCL. Participants in this workshop included computer scientists,
educational psychologists and learning scientists who used a range of theoretical
perspectives in their research, including information processing, socio-cognitive and socio-
cultural perspectives. This study examined coding schemes from leading researchers who
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