Computational dynamic support model for social support assignments around stressed individuals among graduate students

Configuring the best resources for optimal overall performance is one of the challenging topics in Computer Science domains. Within the domain of intelligent social support assignment applications to help individuals with stress, it requires important aspects of configuring a possible set of input and parameters to obtain optimal solutions from both computational support provider and recipient models. However, the existing configuration algorithms are often randomized and static. Thus, their results can vary significantly between multiple runs. In the context of social support perspectives, the assigned support
may not sufficient or cause a burden to the providers. Hence, this study aims to develop the dynamic configuration algorithm to provide an optimal support assignment based on information generated from both social support recipient and provision computational models. The computational models that simulate support providers and recipients behaviours were developed to generate several simulated patterns. These models explain
the dynamics of support seeking and provision behaviours and were evaluated using equilibria analysis and automatic logical verification approaches for 14 selected empirical cases. Later, the dynamic configuration algorithm was designed to utilize possible support assignments based on support provision requirements. The algorithm complexity analysis was used to measure the execution time in the worst case. Finally, a prototype was developed and validated with 30 graduate students. This study allows to explore computational analysis in explicit comprehension of how seeking and giving support
process can be obtained at different case conditions. Also, the study explicitly shows the psychological stress of support recipient can be reduced after the dynamic configuration
algorithm process assigned selected social support providers from social support network members. Furthermore, this study provides an alternative method for software engineers in
intelligent stress management systems to integrate social support-based concepts as one of the mechanisms in addressing the support of an individual with cognitive related stress.