Digital tracing and analysis of collaborative learning with knowledge objects (DigiT)
The DigiT is a network project that aims to advance the digital analysis of collaborative learning with knowledge objects and students’ development of collaborative competence.
In this project an enriched understanding of complex processes of collaboration will be achieved by exploring the analysis of digital data in combination with traditional data types. Photo: Pixabay
About the project
In current education, students are expected to engage often in collaborative activities and to produce knowledge objects, which are artefacts encompassing their (usually disciplinary) knowledge (for example, assignments, essays, research or project reports, etc.). These collaborative activities are of importance, as students’ learning performance is often assessed based on the knowledge objects produced. Yet, there is little research about the way these object-oriented collaborative activities take place and how they can be supported. Developing methodologies and approaches to trace and analyse data collected digitally enables novel ways to investigate how students engage in the process and how they employ digital technologies to create such objects collaboratively. A specific challenge is represented by the difficulty of scaling up the tracing and analyzing processes rigorously.
The DigiT is a network project that aims to advance the digital analysis of collaborative learning with knowledge objects and students’ development of collaborative competence. An enriched understanding of complex processes of collaboration will be achieved by exploring the analysis of digital data in combination with traditional data types.
To better understanding of practices of object-oriented collaboration, DigiT aims to:
- Advance understanding of learning practices involved in students’ object-oriented collaboration;
- Develop approaches based on learning analytics and automated analysis techniques to trace and interpret object-oriented collaboration at various levels (individual, collective and institutional); and
- Contribute to the development of indicators for measuring and assessing competences for object-oriented collaboration.
The planned work will include analytical explorations that combine qualitative approaches with learning analytics and automated analysis techniques. This analytical approach will allow examination of digital traces using: social and epistemic network analysis of network and digital trace data, i.e., online discussions, aggregated collaboration and artefact development contributions, experienced interaction; dynamic bayesian networks and breakpoint analysis of temporal manifestation in interaction data; automated content analysis of developing knowledge objects, produced collectively, versioning, commenting), inspired by qualitative analyses and text data mining techniques.
The work of the collaborating teams will build on the coexistence of both prior knowledge and newly acquired conceptions and ideas about how collaborative learning can be captured in its complexity. The project will contribute to theoretical, methodological and ethical knowledge on data analytics practices.
The DigiT project is funded by the European Association for Research on Learning and Instruction (EARLI) and has the status of Center of Exellence (E-CER)
Damşa, C. & Muukkonen, H. (2019). Conceptualizing pedagogies for learning through object-oriented collaboration in higher education. Research Papers in Education.