The proposed tool will provide users with a platform to access a side-by-side comparison of classroom assessment and job posting requirements. Using techniques and methodologies from NLP, machine learning, data analysis, and data mining: the employed algorithm analyzes job postings and classroom assessments, extracts and classifies skill units within, then compares sets of skills from different input volumes. This effectively provides a predicted alignment between academic and career sources, both federal and industrial. The compilation of tool results indicates an overall accuracy score of 82%, and an alignment score of only 75.5% between the input assessments and overall job postings. These results describe that the 50 UNT assessments and 5,000 industry and federal job postings examined, demonstrate a compatibility (alignment) of 75.5%; and, that this measure was calculated using a tool operating at an 82% precision rate.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc2179294 |
| Date | 07 1900 |
| Creators | Parks, Tyler J. |
| Contributors | Dantu, Ram, Pears, Russel, Shah, Sayed K. |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Parks, Tyler J., Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
Page generated in 0.0024 seconds