Integrated Curriculum Analytics: Bridging Structure, Pass Rates, and Student Outcomes
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| Title: | Integrated Curriculum Analytics: Bridging Structure, Pass Rates, and Student Outcomes |
|---|---|
| Language: | English |
| Authors: | Ahmad Slim, Chaouki Abdallah, Elisha Allen, Michael Hickman, Ameer Slim |
| Source: | International Educational Data Mining Society. 2025. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Peer Reviewed: | Y |
| Page Count: | 7 |
| Publication Date: | 2025 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Curriculum Design, Integrated Curriculum, Data Analysis, Monte Carlo Methods, Algorithms, Outcomes of Education, Graduation Rate, Higher Education |
| Abstract: | Curricular design in higher education significantly impacts student success and institutional performance. However, academic programs' complexity--shaped by pass rates, prerequisite dependencies, and course repeat policies--creates challenges for administrators. This paper presents a method for modeling curricular pathways including development of a "Curricular Analytics App," a scalable platform that models curricula as directed acyclic graphs (DAGs) to detect structural inefficiencies and bottlenecks. This method integrates Critical Path Analysis to highlight bottleneck courses delaying student progression, enhanced Monte Carlo simulations to capture real-world variability in course pass rates and retakes, and introduces "Passability Complexity," a novel metric incorporating probabilistic pass rates into structural complexity. These features provide deeper insights into curriculum difficulty and graduation timelines. As a proof of concept that allows for applied analysis, the "Curricular Analytics App" has an interactive interface which users can modify courses and prerequisites in real time, enabling data-driven curriculum optimization. The app's efficient graph-based algorithms ensure scalability for large academic programs. By linking curriculum structure to student outcomes, it supports institutions in improving graduation rates and streamlining degree pathways through evidence-based decision-making. [For the complete proceedings, see ED675583.] |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | ED675608 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED675608 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: Integrated Curriculum Analytics: Bridging Structure, Pass Rates, and Student Outcomes – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ahmad+Slim%22">Ahmad Slim</searchLink><br /><searchLink fieldCode="AR" term="%22Chaouki+Abdallah%22">Chaouki Abdallah</searchLink><br /><searchLink fieldCode="AR" term="%22Elisha+Allen%22">Elisha Allen</searchLink><br /><searchLink fieldCode="AR" term="%22Michael+Hickman%22">Michael Hickman</searchLink><br /><searchLink fieldCode="AR" term="%22Ameer+Slim%22">Ameer Slim</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22International+Educational+Data+Mining+Society%22"><i>International Educational Data Mining Society</i></searchLink>. 2025. – Name: Avail Label: Availability Group: Avail Data: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 7 – Name: DatePubCY Label: Publication Date Group: Date Data: 2025 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Speeches/Meeting Papers<br />Reports - Research – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Curriculum+Design%22">Curriculum Design</searchLink><br /><searchLink fieldCode="DE" term="%22Integrated+Curriculum%22">Integrated Curriculum</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+Methods%22">Monte Carlo Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Outcomes+of+Education%22">Outcomes of Education</searchLink><br /><searchLink fieldCode="DE" term="%22Graduation+Rate%22">Graduation Rate</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Curricular design in higher education significantly impacts student success and institutional performance. However, academic programs' complexity--shaped by pass rates, prerequisite dependencies, and course repeat policies--creates challenges for administrators. This paper presents a method for modeling curricular pathways including development of a "Curricular Analytics App," a scalable platform that models curricula as directed acyclic graphs (DAGs) to detect structural inefficiencies and bottlenecks. This method integrates Critical Path Analysis to highlight bottleneck courses delaying student progression, enhanced Monte Carlo simulations to capture real-world variability in course pass rates and retakes, and introduces "Passability Complexity," a novel metric incorporating probabilistic pass rates into structural complexity. These features provide deeper insights into curriculum difficulty and graduation timelines. As a proof of concept that allows for applied analysis, the "Curricular Analytics App" has an interactive interface which users can modify courses and prerequisites in real time, enabling data-driven curriculum optimization. The app's efficient graph-based algorithms ensure scalability for large academic programs. By linking curriculum structure to student outcomes, it supports institutions in improving graduation rates and streamlining degree pathways through evidence-based decision-making. [For the complete proceedings, see ED675583.] – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2025 – Name: AN Label: Accession Number Group: ID Data: ED675608 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED675608 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 7 Subjects: – SubjectFull: Curriculum Design Type: general – SubjectFull: Integrated Curriculum Type: general – SubjectFull: Data Analysis Type: general – SubjectFull: Monte Carlo Methods Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Outcomes of Education Type: general – SubjectFull: Graduation Rate Type: general – SubjectFull: Higher Education Type: general Titles: – TitleFull: Integrated Curriculum Analytics: Bridging Structure, Pass Rates, and Student Outcomes Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ahmad Slim – PersonEntity: Name: NameFull: Chaouki Abdallah – PersonEntity: Name: NameFull: Elisha Allen – PersonEntity: Name: NameFull: Michael Hickman – PersonEntity: Name: NameFull: Ameer Slim IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Titles: – TitleFull: International Educational Data Mining Society Type: main |
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