Teamwork Distribution: Local vs. Global Software Engineering Project Development Teamwork
DOI:
https://doi.org/10.3991/ijet.v15i18.15489Keywords:
E-learning, Software engineering, Project development, Machine learning, Project failure, Teamwork distributionAbstract
Deliverable and course project become the preferred mean to measure learner competency and attainment of intended learning outcomes in IT-fields. Proper setup and evaluation for teamwork projects remains a key challenge for e-learning systems. This study investigates the possibility to improve the early prediction of academic software engineering project failure by treating teamwork differently according to the distribution of teamwork participants. Two configurations of teamwork distribution are considered. In the first configuration, a teamwork may include international participants, but all team participants are affiliated to the same institution, namely local teamwork. In the second configuration, a teamwork may include participants from different institutions, namely global teamwork. Software engineering projects are approached from two distinct perspectives. First, obeying the best practices during the system development life cycle (SDLC), namely, process perspective. Second, characteristics of the final deliverable deployed at each milestone of the SDLC, namely product perspective. A publicly released dataset collected by a designated e-learning environment is leveraged to validate the proposed approach. Results indicate a noticeable variance among local and global distributions. These results puts evidence that the reasons behind software engineering teamwork project failures may vary depending on the distribution of the teamwork, local vs. global. Consequently, it advise to customize e-learning systems differently according to the teamwork distribution.
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Published
2020-09-25
How to Cite
Al-Taharwa, I. (2020). Teamwork Distribution: Local vs. Global Software Engineering Project Development Teamwork. International Journal of Emerging Technologies in Learning (iJET), 15(18), pp. 183–201. https://doi.org/10.3991/ijet.v15i18.15489
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Papers