Identifying Courses for Targeted Review Using GAP Analysis and Machine Learning.
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| Title: | Identifying Courses for Targeted Review Using GAP Analysis and Machine Learning. |
|---|---|
| Authors: | Joseph, Kishore1 (AUTHOR) kishore.joseph@siu.edu, Calvert, Wesley C.2 (AUTHOR), Keys Jr., Oliver3 (AUTHOR), McCrocklin, Shannon M.4 (AUTHOR) |
| Source: | Education Sciences. May2026, Vol. 16 Issue 5, p806. 24p. |
| Subject Terms: | *Course evaluation (Education), *Academic achievement, *Machine learning, *Educational quality, Artificial neural networks, Deviation (Statistics), Resource allocation, Risk assessment |
| Abstract: | We examined the limitations of observed course DFWI rates (% of D and F grades, withdrawals, and incompletes) as evaluation metrics, which obscure student characteristics, course design, instruction, structure, and latent factors, posing challenges in identifying courses that need improvement. An artificial neural network (ANN) was trained using student data to model risk, accounting for variations in student characteristics. The model's predictions on test data were averaged at the course level, producing expected DFWI rates based on student composition. Courses with high observed DFWI rates and large deviations between observed and predicted DFWI rates (the GAP) were ranked and prioritized for review, as they may reflect aspects of course design, structure, or instructional practices warranting further qualitative evaluation. Our predictions are non-causal, and modeling calibration varies across subgroups; therefore, the original GAP rankings, robust to a post-hoc calibration check, are presented as risk-adjusted indicators for prioritizing courses for further review rather than as definitive causal measures of course quality. Rankings based on observed DFWI rates differ substantially from risk-adjusted GAP rankings, indicating that relying on observed DFWI rates alone may misidentify high-risk courses. Our methodology can assist educators and administrators in making fair resource allocation decisions and improving student outcomes. [ABSTRACT FROM AUTHOR] |
| Copyright of Education Sciences is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 194185540 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Identifying Courses for Targeted Review Using GAP Analysis and Machine Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Joseph%2C+Kishore%22">Joseph, Kishore</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kishore.joseph@siu.edu</i><br /><searchLink fieldCode="AR" term="%22Calvert%2C+Wesley+C%2E%22">Calvert, Wesley C.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Keys+Jr%2E%2C+Oliver%22">Keys Jr., Oliver</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22McCrocklin%2C+Shannon+M%2E%22">McCrocklin, Shannon M.</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Education+Sciences%22">Education Sciences</searchLink>. May2026, Vol. 16 Issue 5, p806. 24p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Course+evaluation+%28Education%29%22">Course evaluation (Education)</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+quality%22">Educational quality</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Deviation+%28Statistics%29%22">Deviation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We examined the limitations of observed course DFWI rates (% of D and F grades, withdrawals, and incompletes) as evaluation metrics, which obscure student characteristics, course design, instruction, structure, and latent factors, posing challenges in identifying courses that need improvement. An artificial neural network (ANN) was trained using student data to model risk, accounting for variations in student characteristics. The model's predictions on test data were averaged at the course level, producing expected DFWI rates based on student composition. Courses with high observed DFWI rates and large deviations between observed and predicted DFWI rates (the GAP) were ranked and prioritized for review, as they may reflect aspects of course design, structure, or instructional practices warranting further qualitative evaluation. Our predictions are non-causal, and modeling calibration varies across subgroups; therefore, the original GAP rankings, robust to a post-hoc calibration check, are presented as risk-adjusted indicators for prioritizing courses for further review rather than as definitive causal measures of course quality. Rankings based on observed DFWI rates differ substantially from risk-adjusted GAP rankings, indicating that relying on observed DFWI rates alone may misidentify high-risk courses. Our methodology can assist educators and administrators in making fair resource allocation decisions and improving student outcomes. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Education Sciences is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/educsci16050806 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 806 Subjects: – SubjectFull: Course evaluation (Education) Type: general – SubjectFull: Academic achievement Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Educational quality Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Deviation (Statistics) Type: general – SubjectFull: Resource allocation Type: general – SubjectFull: Risk assessment Type: general Titles: – TitleFull: Identifying Courses for Targeted Review Using GAP Analysis and Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Joseph, Kishore – PersonEntity: Name: NameFull: Calvert, Wesley C. – PersonEntity: Name: NameFull: Keys Jr., Oliver – PersonEntity: Name: NameFull: McCrocklin, Shannon M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22277102 Numbering: – Type: volume Value: 16 – Type: issue Value: 5 Titles: – TitleFull: Education Sciences Type: main |
| ResultId | 1 |
