Potential Abstract: This research article presents an innovative approach to the assessment of transnational education through the implementation of a genetic scoring inference model. The increasing number of students engaged in transnational learning necessitates the development of reliable and efficient methods for evaluating their educational outcomes. The proposed model leverages the power of genetic algorithms, scoring techniques, and the collaborative potential of GitHub to address this challenge.
The genetic scoring inference model integrates genetic algorithms into the scoring process, enabling the identification of optimal scoring configurations that enhance the accuracy of transnational education evaluations. By modeling the assessment process as an evolutionary optimization problem, the model evolves towards an optimal solution, utilizing genetic operators such as selection, crossover, and mutation. This allows for the identification of the most effective scoring criteria and the elimination of suboptimal components, leading to improved educational assessment outcomes.
To facilitate collaboration, transparency, and reproducibility, the model implementation is made available on GitHub, a widely-used platform for code sharing and version control. The GitHub repository includes the complete source code, datasets, and documentation necessary for educators, researchers, and assessment practitioners to apply and adapt the model to their own transnational educational contexts. This open-source approach fosters the sharing of best practices, encourages community involvement, and promotes the continuous improvement of the genetic scoring inference model.
The proposed genetic scoring inference model contributes to the field of transnational education assessment by offering a data-driven and adaptive approach that enhances the accuracy and fairness of evaluations. By leveraging the power of genetic algorithms and the collaborative potential of GitHub, the model facilitates a dynamic and iterative assessment process that aligns with the evolving nature of transnational education. Furthermore, the open-source nature of the model promotes the democratization of assessment practices and encourages interdisciplinary collaboration among researchers, educators, and assessment specialists.
Potential References:
- Cataloging github repositories
- Speed up genetic algorithms in the cloud using software containers
- A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs
- A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space
- Fast Genetic Algorithm for feature selection—A qualitative approximation approach
