Assessing the Performance of Online Adjunct Instructor Candidates: Process Improvements and Lessons Learned
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| Title: | Assessing the Performance of Online Adjunct Instructor Candidates: Process Improvements and Lessons Learned |
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| Language: | English |
| Authors: | Stokes, Steve, McLane, Chad, Jones, Brian L. |
| Source: | Online Journal of Distance Learning Administration. Win 2021 24(4). |
| Availability: | State University of West Georgia. 1601 Maple Street, Honors House, Carrollton, GA 30118. Tel: 678-839-5489; Fax: 678-839-0636; e-mail: distance@westga.edu; Web site: https://ojdla.com/ |
| Peer Reviewed: | Y |
| Page Count: | 8 |
| Publication Date: | 2021 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Teacher Evaluation, Teacher Selection, Adjunct Faculty, Performance Based Assessment, Validity, Reliability, Item Response Theory, College Faculty, Job Applicants, Efficiency, Online Courses, Rating Scales, Private Colleges, Religious Colleges |
| ISSN: | 1556-3847 |
| Abstract: | In this study, we outlined some of the obstacles associated with screening and identifying high-quality online adjunct instructor candidates and described the steps that we took to improve our screening and hiring process. We discussed issues associated with collecting and using performance ratings in the context of a remote screening and hiring process. We also demonstrated a novel application of the Many Facet Rasch (MFR) model to ratings of online adjunct instructor candidate performance. The MFR model was useful for validating the results of our processes improvements. Investing in streamlining and focusing our Candidate Assessment Course (CAS) yielded screening results that were more valid and reliable while requiring fewer administrative resources. |
| Abstractor: | As Provided |
| Entry Date: | 2022 |
| Access URL: | https://ojdla.com/assets/pdf/stokes_mclane_jones244.pdf |
| Accession Number: | EJ1334973 |
| Database: | ERIC |
| Abstract: | In this study, we outlined some of the obstacles associated with screening and identifying high-quality online adjunct instructor candidates and described the steps that we took to improve our screening and hiring process. We discussed issues associated with collecting and using performance ratings in the context of a remote screening and hiring process. We also demonstrated a novel application of the Many Facet Rasch (MFR) model to ratings of online adjunct instructor candidate performance. The MFR model was useful for validating the results of our processes improvements. Investing in streamlining and focusing our Candidate Assessment Course (CAS) yielded screening results that were more valid and reliable while requiring fewer administrative resources. |
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| ISSN: | 1556-3847 |
