BELOW THE LINE: LIVING WITH MINIMUM WAGE
Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.12, No. 6)Publication Date: 2023-06-30
Authors : Abelgas Kyle Leo; Gomez Johannes Vince Doller; Pagkalinawan Miguel Siy; Ortega Alejandro R; Froilan E. De Guzman;
Page : 53-63
Keywords : ;
Abstract
“Minimum wage” is an issue for people in that the current amount tends to not be enough for sustainable and comfortable living, even more so with the eventual threat of inflation as it leaves “low-income” workers even further behind. If people must be made aware of it, one possible option could be to create a game that could potentially educate people on the topic. In this study, we use the “agile methodology” aim to look into some of these issues, while also create a sort of “simulator game” out of it, and then make a survey to see if said game can also provide insight to respondents and determine how aware they are of these issues, as well as if the software we use achieves the requirements of “ISO 25010”. The game itself would have players work about 4 minimum wage jobs in the form of minigames and completing a number of them could earn the player money that they have to save up as they spend it on important needs such as daily groceries and monthly utilities. Apart from that, Players will also need to find time to maintain their family relationships, otherwise they risk losing the game, same as with failing to pay groceries and utilities. This study showed that people are well aware of the issues surrounding minimum wage, and would even approve of its increase, regardless of their current salary.
Other Latest Articles
- CLINICAL PROFILE OF ASCTES BASED ON PRESENTATION AND LABORATORY FINDINGS:AN INSTITUTIONAL EXPERIENCE FROM MAHARASHTRA,INDIA
- WHERE NATURE MEETS LUXURY: ISLAMIC TOURISM PRODUCT INDICATORS FOR ECO-GLAMPING (ITPIEG) SITES TOWARDS SUSTAINABLE ECOTOURISM
- A DEEP LEARNING-BASED ALGORITHM FOR HYBRID APPROACH TO SOLUTION OF MULTIPHYSICS PROBLEMS
- MULTI-FIDELITY CLASSIFICATION USING DEEP LEARNING TO ACCELERATING THE PREDICTION OF LARGE-SCALE COMPUTATIONAL MODELS
- APPLICATION OF MACHINE LEARNING AIDED STOCHASTIC ELASTOPLASTIC ANALYSIS USING AI
Last modified: 2023-07-01 15:02:15