Using Machine Learning to Identify Educational Predictors of Career and Job Satisfaction in Adults with Disabilities
<i>Purpose</i>: This study explored the potential long-term effects of academic-related variables, including academic satisfaction, college degree attainment, unmet academic accommodation needs, and demographic characteristics on the job and career satisfaction of adults with disabilitie...
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MDPI AG
2025-06-01
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| author | Beau LeBlond Bryan R. Christ Benjamin Ertman Olivia Chapman Rea Pillai Paul B. Perrin |
| author_facet | Beau LeBlond Bryan R. Christ Benjamin Ertman Olivia Chapman Rea Pillai Paul B. Perrin |
| author_sort | Beau LeBlond |
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| description | <i>Purpose</i>: This study explored the potential long-term effects of academic-related variables, including academic satisfaction, college degree attainment, unmet academic accommodation needs, and demographic characteristics on the job and career satisfaction of adults with disabilities using modern machine learning techniques. <i>Method</i>: Participants (<i>n</i> = 409) completed an online survey assessing these constructs. All had a disability or chronic health condition, had attended school in the U.S. throughout their K-12 education, and were between 19 and 86 years of age. <i>Results</i>: The random forest models had 68.6% accuracy in correctly identifying job satisfaction and 72.5% accuracy in correctly identifying career satisfaction. When using mean decrease in impurity (MDI) and permutation importance to identify statistical predictors, academic satisfaction was the most important predictor of job satisfaction in both MDI and permutation importance, while unmet academic accommodations was the fourth highest predictor for MDI behind academic satisfaction, disability level, and age, but ahead of other demographic variables and college degree status, and the second highest predictor of job satisfaction in permutation importance. For career satisfaction, academic satisfaction accounted for the highest MDI, while unmet academic accommodations ranked fourth. For permutation importance, academic satisfaction ranked first, and unmet academic accommodations ranked fifth behind academic satisfaction, age, college degree status, and disability level. <i>Discussion</i>: Meeting the academic accommodation needs of disabled students is linked with lasting vocational success. This study underscores the associations between unmet academic accommodation needs and future job and career satisfaction, illuminated using novel machine learning techniques. To our knowledge, this is the first investigation of the potential long-term associations between unfulfilled accommodation needs and future job and career satisfaction. |
| format | Article |
| id | doaj-art-6146383de1d54ba4aef6fbe634df46d3 |
| institution | Kabale University |
| issn | 2673-7272 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Disabilities |
| spelling | doaj-art-6146383de1d54ba4aef6fbe634df46d32025-08-20T03:27:10ZengMDPI AGDisabilities2673-72722025-06-01525610.3390/disabilities5020056Using Machine Learning to Identify Educational Predictors of Career and Job Satisfaction in Adults with DisabilitiesBeau LeBlond0Bryan R. Christ1Benjamin Ertman2Olivia Chapman3Rea Pillai4Paul B. Perrin5School of Data Science, University of Virginia, Charlottesville, VA 22903, USASchool of Data Science, University of Virginia, Charlottesville, VA 22903, USADepartment of Psychology, University of Virginia, Charlottesville, VA 22904, USADepartment of Psychology, University of Virginia, Charlottesville, VA 22904, USADepartment of Psychology, University of Virginia, Charlottesville, VA 22904, USASchool of Data Science, University of Virginia, Charlottesville, VA 22903, USA<i>Purpose</i>: This study explored the potential long-term effects of academic-related variables, including academic satisfaction, college degree attainment, unmet academic accommodation needs, and demographic characteristics on the job and career satisfaction of adults with disabilities using modern machine learning techniques. <i>Method</i>: Participants (<i>n</i> = 409) completed an online survey assessing these constructs. All had a disability or chronic health condition, had attended school in the U.S. throughout their K-12 education, and were between 19 and 86 years of age. <i>Results</i>: The random forest models had 68.6% accuracy in correctly identifying job satisfaction and 72.5% accuracy in correctly identifying career satisfaction. When using mean decrease in impurity (MDI) and permutation importance to identify statistical predictors, academic satisfaction was the most important predictor of job satisfaction in both MDI and permutation importance, while unmet academic accommodations was the fourth highest predictor for MDI behind academic satisfaction, disability level, and age, but ahead of other demographic variables and college degree status, and the second highest predictor of job satisfaction in permutation importance. For career satisfaction, academic satisfaction accounted for the highest MDI, while unmet academic accommodations ranked fourth. For permutation importance, academic satisfaction ranked first, and unmet academic accommodations ranked fifth behind academic satisfaction, age, college degree status, and disability level. <i>Discussion</i>: Meeting the academic accommodation needs of disabled students is linked with lasting vocational success. This study underscores the associations between unmet academic accommodation needs and future job and career satisfaction, illuminated using novel machine learning techniques. To our knowledge, this is the first investigation of the potential long-term associations between unfulfilled accommodation needs and future job and career satisfaction.https://www.mdpi.com/2673-7272/5/2/56disabilitycareer satisfactionjob satisfactioneducational accommodationsmachine learning |
| spellingShingle | Beau LeBlond Bryan R. Christ Benjamin Ertman Olivia Chapman Rea Pillai Paul B. Perrin Using Machine Learning to Identify Educational Predictors of Career and Job Satisfaction in Adults with Disabilities Disabilities disability career satisfaction job satisfaction educational accommodations machine learning |
| title | Using Machine Learning to Identify Educational Predictors of Career and Job Satisfaction in Adults with Disabilities |
| title_full | Using Machine Learning to Identify Educational Predictors of Career and Job Satisfaction in Adults with Disabilities |
| title_fullStr | Using Machine Learning to Identify Educational Predictors of Career and Job Satisfaction in Adults with Disabilities |
| title_full_unstemmed | Using Machine Learning to Identify Educational Predictors of Career and Job Satisfaction in Adults with Disabilities |
| title_short | Using Machine Learning to Identify Educational Predictors of Career and Job Satisfaction in Adults with Disabilities |
| title_sort | using machine learning to identify educational predictors of career and job satisfaction in adults with disabilities |
| topic | disability career satisfaction job satisfaction educational accommodations machine learning |
| url | https://www.mdpi.com/2673-7272/5/2/56 |
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