Moving this comment by abiodunyusufmashood-dot from another issue:
I am working on a data model titled "High Impact Practices (HIPs) Profile" as a concrete Learning Opportunity Use Case.
Research has firmly established that experiential-based HIPs (such as internships, Service-Learning, Community-Based Learning, and capstone projects) correlate significantly with student engagement, learning retention, completion, and eventual employment. Consequently, HIPs have been widely embraced across higher education as verified "Quality Markers." To ensure that student participation in these practices reflects true academic rigor rather than just a nominal label, Finley’s comprehensive assessment model (2019) emphasizes the critical need for verifiable evidence regarding quality elements, learning achievements, and workforce outcomes. The absolute necessity for these empirical evidence datasets has also been heavily underscored in recent workforce-readiness frameworks, including the Strada State Opportunity Index and the Trellis Career Impact Audit.
To make these HIP datasets globally discoverable and machine-readable, I have modeled the HIPsProfile as a subclass of ceterms:LearningOpportunityProfile.
Data Model Architecture & Properties:
Inherited Properties: The profile leverages existing CTDL properties such as ceterms:requires and ceterms:conditionProfile to link the HIP to targeted quality elements and competencies.
Proposed Extensions: The model introduces two new properties: learningAchievementEvidence and learningQualityEvidence, which map directly to Learning Achievement and Learning Quality resources.
Evidence Validation: Both the Learning Achievement and Learning Quality constructs utilize ceterms:hasRubric and relevant qdata properties (relevantDataSet) to publish institutional rubrics and verified achievement datasets.
Subclassing and Dimensional Modeling:
To handle the structural nuances of different experiential models, the core HIPsProfile is further subclassed into three specialized profiles:
WorkBasedLearning: Captures dimensions such as paid internships, micro-internships, apprenticeships, co-op education, clinical rotations, and student employment.
ResearchBasedLearning: Captures dimensions such as undergraduate research, capstone courses/projects, and collaborative research projects.
CommunityGlobalLearning: Captures dimensions such as service-learning, community-based learning, and global/study abroad learning.
The Technical Use Case for DatasetProfile Dimensions:
Architecturally, I am leveraging qdata:DatasetProfile dimensions to capture institutional participation datasets distinctly for each specific variant (e.g., separating micro-internship numbers from standard apprenticeships). Concurrently, the overriding learning achievement data and learning quality data are captured collectively at the subclass profile level. This prevents data fragmentation while preserving granular reporting.
Earlier discussions with Jeane Kitchens have been immensely valuable in refining this model. I believe this specific use case aligns directly with the conceptualization of Learning Opportunity as a Specification discussed by Phil in the April CTDL TAG presentation (within the Learning Opportunities Complexity section).
I welcome the group's input, feedback, and technical clarifications on this model as we look to expand the CTDL’s capability in capturing experiential quality markers.
Thanks,
Yusuf Mashood Abiodun PhD Researcher, Credential Quality Frameworks
Moving this comment by abiodunyusufmashood-dot from another issue:
I am working on a data model titled "High Impact Practices (HIPs) Profile" as a concrete Learning Opportunity Use Case.
Research has firmly established that experiential-based HIPs (such as internships, Service-Learning, Community-Based Learning, and capstone projects) correlate significantly with student engagement, learning retention, completion, and eventual employment. Consequently, HIPs have been widely embraced across higher education as verified "Quality Markers." To ensure that student participation in these practices reflects true academic rigor rather than just a nominal label, Finley’s comprehensive assessment model (2019) emphasizes the critical need for verifiable evidence regarding quality elements, learning achievements, and workforce outcomes. The absolute necessity for these empirical evidence datasets has also been heavily underscored in recent workforce-readiness frameworks, including the Strada State Opportunity Index and the Trellis Career Impact Audit.
To make these HIP datasets globally discoverable and machine-readable, I have modeled the HIPsProfile as a subclass of ceterms:LearningOpportunityProfile.
Data Model Architecture & Properties:
Subclassing and Dimensional Modeling:
To handle the structural nuances of different experiential models, the core HIPsProfile is further subclassed into three specialized profiles:
The Technical Use Case for DatasetProfile Dimensions:
Architecturally, I am leveraging qdata:DatasetProfile dimensions to capture institutional participation datasets distinctly for each specific variant (e.g., separating micro-internship numbers from standard apprenticeships). Concurrently, the overriding learning achievement data and learning quality data are captured collectively at the subclass profile level. This prevents data fragmentation while preserving granular reporting.
Earlier discussions with Jeane Kitchens have been immensely valuable in refining this model. I believe this specific use case aligns directly with the conceptualization of Learning Opportunity as a Specification discussed by Phil in the April CTDL TAG presentation (within the Learning Opportunities Complexity section).
I welcome the group's input, feedback, and technical clarifications on this model as we look to expand the CTDL’s capability in capturing experiential quality markers.
Thanks,
Yusuf Mashood Abiodun PhD Researcher, Credential Quality Frameworks