Summary of Applying Data Driven Decision Making to Rank Vocational and Educational Training Programs with Topsis, by J. M. Conejero et al.
Applying Data Driven Decision Making to rank Vocational and Educational Training Programs with TOPSIS
by J. M. Conejero, J. C. Preciado, A. E. Prieto, M. C. Bas, V. J. Bolos
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Numerical Analysis (math.NA)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a multi-criteria classification of Vocational and Educational Programs in Extremadura (Spain) from 2009-2016. The authors integrate detailed information about individuals finishing these studies with labor data to create a comprehensive database. They employ the TOPSIS method, combined with a novel decision support technique for assessing criterion influence and dependence on assigned weights. This new approach is based on worst-best case scenario analysis and compared to Pearson’s correlation ratio-based global sensitivity analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper ranks vocational and educational programs in Spain from 2009-2016 using a multi-criteria method. They combine data on students who finished these programs with their labor records, creating a big database. The authors use a special way of looking at this data called TOPSIS, along with a new way to figure out how important each part is and how it affects the overall ranking. This new approach compares two methods for understanding how different factors affect the results. |
Keywords
» Artificial intelligence » Classification
