Evaluation of a Predictive Model -- Montana Early Warning System. Technical Report
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| Title: | Evaluation of a Predictive Model -- Montana Early Warning System. Technical Report |
|---|---|
| Language: | English |
| Authors: | Robin Clausen (ORCID |
| Source: | Grantee Submission. 2023. |
| Peer Reviewed: | Y |
| Page Count: | 87 |
| Publication Date: | 2023 |
| Sponsoring Agency: | National Center for Education Research (NCER) (ED/IES) |
| Contract Number: | R305S210011 |
| Document Type: | Reports - Research |
| Education Level: | Elementary Secondary Education |
| Descriptors: | Identification, Dropouts, At Risk Students, Prediction, Models, Evaluation, Reliability, School Districts, Adoption (Ideas), Elementary Secondary Education |
| Geographic Terms: | Montana |
| Abstract: | Research over the monitoring of at-risk youth. Behavioral interventions have been the primary focus of this literature past two decades has focused on one aspect of dropout prevention: early identification an however, there is renewed emphasis placed on attendance and academic risk factors under ESSA. Recognizing a need to promote early identification, stakeholders within the Montana Office of Public Instruction (OPI) framed a comprehensive system of supports in FY 2013, principally a diagnostic tool, which allows for the linkage of data to intervention and the ability to target resources where the intervention is most likely to be effective. Such supports became known as the Montana Early Warning System (EWS). This evaluation is based on three separate tasks. The first of which is an analysis of the functional process of predicting dropout and graduation. Does the system predict as reliably dropout and graduation? We know the system predicts dropout well based on the process of refinement of the model. However, we do not know if the model reliably predicts graduation. The second task focuses on analyzing the degree of implementation of the program by creating a classification of low, medium, and high adopters. It further analyzes mediating and moderating factors to the implementation, the quality of the tool, the role of interventions, and the effectiveness of the tool. The third task is primarily quantitative and focuses on subgroups identified in the analysis. |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2025 |
| Access URL: | https://opi.mt.gov/Leadership/Data-Reporting/Research-Portal#9965312917-evaluation-of-a-predictive-model-montana-early-warning-system-2023 |
| Accession Number: | ED672196 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED672196 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: Evaluation of a Predictive Model -- Montana Early Warning System. Technical Report – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Robin+Clausen%22">Robin Clausen</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0005-4212-0224">0009-0005-4212-0224</externalLink>)<br /><searchLink fieldCode="AR" term="%22Montana+Office+of+Public+Instruction%22">Montana Office of Public Instruction</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2023. – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 87 – Name: DatePubCY Label: Publication Date Group: Date Data: 2023 – Name: SourceSuprt Label: Sponsoring Agency Group: SrcSuprt Data: National Center for Education Research (NCER) (ED/IES) – Name: NumberContract Label: Contract Number Group: NumCntrct Data: R305S210011 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Reports - Research – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Elementary+Secondary+Education%22">Elementary Secondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Identification%22">Identification</searchLink><br /><searchLink fieldCode="DE" term="%22Dropouts%22">Dropouts</searchLink><br /><searchLink fieldCode="DE" term="%22At+Risk+Students%22">At Risk Students</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation%22">Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Reliability%22">Reliability</searchLink><br /><searchLink fieldCode="DE" term="%22School+Districts%22">School Districts</searchLink><br /><searchLink fieldCode="DE" term="%22Adoption+%28Ideas%29%22">Adoption (Ideas)</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+Secondary+Education%22">Elementary Secondary Education</searchLink> – Name: Subject Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Montana%22">Montana</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Research over the monitoring of at-risk youth. Behavioral interventions have been the primary focus of this literature past two decades has focused on one aspect of dropout prevention: early identification an however, there is renewed emphasis placed on attendance and academic risk factors under ESSA. Recognizing a need to promote early identification, stakeholders within the Montana Office of Public Instruction (OPI) framed a comprehensive system of supports in FY 2013, principally a diagnostic tool, which allows for the linkage of data to intervention and the ability to target resources where the intervention is most likely to be effective. Such supports became known as the Montana Early Warning System (EWS). This evaluation is based on three separate tasks. The first of which is an analysis of the functional process of predicting dropout and graduation. Does the system predict as reliably dropout and graduation? We know the system predicts dropout well based on the process of refinement of the model. However, we do not know if the model reliably predicts graduation. The second task focuses on analyzing the degree of implementation of the program by creating a classification of low, medium, and high adopters. It further analyzes mediating and moderating factors to the implementation, the quality of the tool, the role of interventions, and the effectiveness of the tool. The third task is primarily quantitative and focuses on subgroups identified in the analysis. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: CodeSource Label: IES Funded Group: SrcInfo Data: Yes – Name: DateEntry Label: Entry Date Group: Date Data: 2025 – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://opi.mt.gov/Leadership/Data-Reporting/Research-Portal#9965312917-evaluation-of-a-predictive-model-montana-early-warning-system-2023" linkWindow="_blank">https://opi.mt.gov/Leadership/Data-Reporting/Research-Portal#9965312917-evaluation-of-a-predictive-model-montana-early-warning-system-2023</link> – Name: AN Label: Accession Number Group: ID Data: ED672196 |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 87 Subjects: – SubjectFull: Identification Type: general – SubjectFull: Dropouts Type: general – SubjectFull: At Risk Students Type: general – SubjectFull: Prediction Type: general – SubjectFull: Models Type: general – SubjectFull: Evaluation Type: general – SubjectFull: Reliability Type: general – SubjectFull: School Districts Type: general – SubjectFull: Adoption (Ideas) Type: general – SubjectFull: Elementary Secondary Education Type: general – SubjectFull: Montana Type: general Titles: – TitleFull: Evaluation of a Predictive Model -- Montana Early Warning System. Technical Report Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Montana Office of Public Instruction – PersonEntity: Name: NameFull: Robin Clausen IsPartOfRelationships: – BibEntity: Dates: – D: 12 M: 07 Type: published Y: 2023 Titles: – TitleFull: Grantee Submission Type: main |
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