Abstract

This paper demonstrates how cognitive load theory can be used to improve learning outcomes by presenting a tool capable of assisting novices to learn to model sequence diagrams effectively. Sequence diagrams are known to lead to heavy cognitive load as they must be consistent with the class diagram, while discharging all the responsibilities specified in the underlying use case. Moreover, novices must also consider the various design options and their impact on the qualitative aspects of the model. Our tool allows cognitive load to be better managed by using a ‘divide and conquer’ approach. In the initial stage students need to focus only on consistency aspects, and they will not be allowed violate the constraints stated in the class diagram. In the second stage, students will not be allowed to submit a diagram until the stated use case goals are met. In the final stage qualitative feedback and marks are awarded based on established metrics and students are allowed to improve their scores by resubmitting the model. Qualitative and quantitative results show that our novel tool using a form of gamification has helped to improve the learning outcomes in modelling substantially, especially for the stragglers. One benefit of our approach is that it can be adapted to other areas where students maybe cognitively challenged.

Recommended Citation

Alhazmi, S., Thevathayan, C., & Hamilton, M. (2021). Improving Learning Outcomes in UML Sequence Diagrams Through Reduced Cognitive Load. In E. Insfran, F. González, S. Abrahão, M. Fernández, C. Barry, H. Linger, M. Lang, & C. Schneider (Eds.), Information Systems Development: Crossing Boundaries between Development and Operations (DevOps) in Information Systems (ISD2021 Proceedings). Valencia, Spain: Universitat Politècnica de València.

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Improving Learning Outcomes in UML Sequence Diagrams Through Reduced Cognitive Load

This paper demonstrates how cognitive load theory can be used to improve learning outcomes by presenting a tool capable of assisting novices to learn to model sequence diagrams effectively. Sequence diagrams are known to lead to heavy cognitive load as they must be consistent with the class diagram, while discharging all the responsibilities specified in the underlying use case. Moreover, novices must also consider the various design options and their impact on the qualitative aspects of the model. Our tool allows cognitive load to be better managed by using a ‘divide and conquer’ approach. In the initial stage students need to focus only on consistency aspects, and they will not be allowed violate the constraints stated in the class diagram. In the second stage, students will not be allowed to submit a diagram until the stated use case goals are met. In the final stage qualitative feedback and marks are awarded based on established metrics and students are allowed to improve their scores by resubmitting the model. Qualitative and quantitative results show that our novel tool using a form of gamification has helped to improve the learning outcomes in modelling substantially, especially for the stragglers. One benefit of our approach is that it can be adapted to other areas where students maybe cognitively challenged.