Collaboration patterns in students' teams working on business cases

Galena Pisoni, Hannie Gijlers, Thu Ha Nguyen, Hsin Chueh Chen

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Abstract

In recent years, computer science education teachers needed to incorporate challenge-based and teambased collaboration projects to enhance learning and prepare students for their future careers. The successful use of team-based work structures depends on the ability of team members to work together with each team developing its own pattern of work. Using affinity propagation as an algorithm, we study the patterns of collaborative behavior from Trello data obtained from 16 teams collaborating on business cases. All the teams were given the same instructions and were asked to use Trello to organize and monitor tasks. Actions of the group members in Trello were categorized as different contribution types, namely activities involving planning, coordination, further input, deletion, or updates on tasks. Sequences of those actions were first created for each group and later used to explore the differences in the working process between the groups. We analyze data and interpret patterns of collaborative work during the entire collaboration on the project, as well as patterns of work that emerged in the first 50 actions in teamwork, since literature indicates that the first actions in teamwork are important for the creation of a "team memory". We present both the initiating sequences along with entire sequences for all teams, and some first results for the different collaboration patterns we observed. This study is the very first exploratory research covering collaboration patterns with Trello data. This kind of pattern analysis could provide teachers with a means to identify teams that need further support in their teamwork. Our data suggest that future studies could complement the analysis with data also coming from other channels used by students for communication and organization of teamwork triangulating data with them.

Original languageEnglish
Title of host publicationL2D 2021. Enabling Data-Driven Decisions from Learning on the Web 2021
Subtitle of host publicationProceedings of the First International Workshop on Enabling Data-Driven Decisions from Learning on the Web co-located with the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021)
Pages14-27
Number of pages14
Volume2876
Publication statusPublished - Mar 2021
Event1st International Workshop on Enabling Data-Driven Decisions from Learning on the Web, L2D 2021 - Virtual, Jerusalem, Israel
Duration: 12 Mar 202112 Mar 2021
Conference number: 1

Publication series

NameCEUR workshop proceedings
PublisherRheinisch Westfälische Technische Hochschule
Volume2876
ISSN (Print)1613-0073

Conference

Conference1st International Workshop on Enabling Data-Driven Decisions from Learning on the Web, L2D 2021
Abbreviated title L2D 2021
CountryIsrael
CityVirtual, Jerusalem
Period12/03/2112/03/21

Keywords

  • Collaboration
  • Innovation & Entrepreneurship
  • Online education
  • Team-based learning
  • TeamWork

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