- Bertrand Schneider, Harvard University, USA. email@example.com
- Kate Thompson, Queensland University of Technology, Australia. firstname.lastname@example.org
- Nia Dowell, University of California Irvine, USA. email@example.com
AIMS & SCOPE
Innovation is vital for economic competitiveness, quality of life, and national security, with increasing reliance on both physical and virtual teams and their collaborative efforts, to solve complex environmental, social and public health problems (Fiore, Graesser, & Greiff, 2018). To contend with these dynamic conditions, collaborative competencies have taken a principal role in educational policy, research, and technology (Graesser et al., 2018). The availability of naturally occurring educational data within existing and emerging collaborative environments presents a unique opportunity to make significant advances in our understanding of learners’ social, cognitive and affective interaction dynamics and collaborative ecologies.
We invite contributions to a special issue of the Journal of Learning Analytics (JLA) on Collaboration Analytics. The goal of this special issue is to build upon promising multimodal sensing technologies, collaborative tools and emerging data analytics techniques to provide researchers with a new lens for understanding human collaboration in a variety of settings.
Recent developments in data collection techniques now allow researchers and practitioners to collect vast amounts of process data on small groups - from click streams to multimodal sensor data (Blikstein & Worsley, 2016), including eye-tracking, natural language processing, motion sensing, or physiological data. This opens new doors to both better understand collaborative mechanisms, and support them in a rigorous and data-driven fashion (Wise & Schwarz, 2017). These opportunities are at the core of this special issue: how can fine-grained data from small groups be leveraged to 1) design better models of collaboration; 2) support interactions between students (e.g., through awareness tools); 3) design innovative ways of teaching collaborative skills, and 4) contribute to theory building (e.g., by expending current frameworks)? These new opportunities have the potential to radically change research on small groups in education, and in the social sciences more generally.
Collecting additional types of data in new contexts to explore social interactions between students, however, brings new challenges. Data privacy and ethics has become a central topic in the learning analytics community (and beyond), particularly when the data is shared between students within a group, but also because the data collected is from students in learning situations which can be challenging, frustrating, and involves students in vulnerable situations. The quality of the data is also a major concern, when “cheap” data is easily available this can influence researchers to focus on less relevant constructs (without leveraging existing learning theories, see concerns raised by Wise & Shaffer, 2015), and common standards for collecting and reporting on learning data do not currently exist. Finally, new approaches also come with new biases (O'Neil, 2016), from black box models that are fine-tuned for specific samples of students and underperform for others, to researchers’ attention being drawn to incomplete or misleading information (i.e., street light effect - Ochoa & Worsley, 2016). These challenges are of special interest for this special issue, especially for contributions that tackle them directly in the context of small groups.
TOPICS OF INTEREST
We welcome contributions from various settings, including: empirical (lab) studies, online platforms, and ecological settings (e.g., classrooms, informal learning environments, workplaces). Possible foci of the submissions include, but are not limited to the following:
- From data to constructs: innovative methods for capturing educational constructs of interest related to students’ collaboration.
- Leveraging new data streams: new ways of capturing collaboration constructs, for example using multimodal sensor data (e.g., eye-tracking, motion, physiological, etc.)
- Computational models: submissions that use new computational algorithms to model collaboration, using various data streams
- Interventionist studies: work that goes beyond describing collaboration and uses data analytics to support collaboration
- Contribution to theory: frameworks or empirical work that employ collaboration analytics to contribute to theory building
- Ethics and privacy: considerations of users’ privacy and ethics when using collaborative analytics in real-world settings, and how it might prevent adoption (e.g., Kitto & Knight, 2019)
Prospective authors may contact the section editors with queries. Final submissions will take place through JLA’s online submission system at http://learning-analytics.info When submitting a paper, select the section “Special Section: Collaboration Analytics". All submissions should follow JLA’s standard manuscript guidelines and template available on the journal website, and will undergo double-blind peer review.
- Dec 2019: Call for papers
- April 10, 2020: Deadline to submit papers
- July, 2020: First round of reviews returned to authors
- Sept 2020: Resubmissions due from authors
- Dec 2020: Second (final) round of reviews returned to authors
- Jan 2021: Final editorial decisions
- Spring 2021: Anticipated Publication
Blikstein, P., & Worsley, M. (2016). Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220-238.
Fiore, S. M., Graesser, A., & Greiff, S. (2018). Collaborative problem-solving education for the twenty-first-century workforce. Nature Human Behaviour, 2(6), 367–369.
Graesser, A. C., Fiore, S. M., Greiff, S., Andrews-Todd, J., Foltz, P. W., & Hesse, F. W. (2018). Advancing the Science of Collaborative Problem Solving. Psychological Science in the Public Interest: A Journal of the American Psychological Society, 19(2), 59–92.
Kitto, K., & Knight, S. (2019). Practical ethics for building learning analytics. British Journal of Educational Technology, 50(6), 2855–2870.
Ochoa, X., & Worsley, M. (2016). Augmenting Learning Analytics with Multimodal Sensory Data. Journal of Learning Analytics, 3(2), 213-219.
O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.
Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 5-13.
Wise, A. F., & Schwarz, B. B. (2017). Visions of CSCL: Eight provocations for the future of the field. International Journal of Computer-Supported Collaborative Learning, 12(4), 423-467.