NSC Student Tracker Integration into Student Journey Model

Overview

• The team presented a pilot project integrating Hartnell’s licensed National Student Clearinghouse (NSC) Student Tracker data into the Student Journey Data Model exclusively for their own use.

• The pilot is being conducted with Hartnell College and Layheng’s team.

• Goal is to provide colleges with a more complete view of student outcomes beyond enrollment at their institution.

Proposed Student Journey Pathway

The model aims to track students through:

• Application

• Enrollment

• Transfer to 2-year institutions

• Transfer to 4-year institutions

• Bachelor's degree completion

• Graduate credential completion

Key Capabilities

• Disaggregation by:

  • Demographics

  • Ethnicity

  • Dual enrollment status

  • Other student subpopulations

• Visualization of student progression through funnel charts and similar reporting tools.

• Student-level tracking to support research and intervention efforts.

Data Sharing Considerations

• Colleges may provide their own NSC data.

• Data can only be shared back with the originating college.

• Current licensing and policy restrictions prevent the DW sharing CCCCO-licensed NSC data directly with colleges or distircts.

Future Enhancements

• Integration of Lightcast employment data.

• Expansion of student pathways to include:

  • Employment outcomes

  • Workforce participation after leaving college

  • Career progression tracking

Hartnell College Use Cases

• Tracking students after they leave Hartnell.

• Understanding transfer patterns.

• Monitoring dual-enrollment student outcomes.

• Supporting intervention efforts when students leave before completion.

• Measuring post-graduation employment and transfer outcomes.

Discussion: Student Flow Analysis and Cross-College Visibility

Victor's Research Interest

Victor described a project examining:

• Students who applied but did not enroll.

• Whether those students enrolled elsewhere.

• Which institutions they attended.

• How intended major and enrollment decisions relate.

Key Discussion Points

• Technical capability appears to exist for broader student flow analysis.

• Policy restrictions currently limit access to cross-college application and enrollment information.

• Participants discussed potential future approaches such as:

  • Data-on-demand services.

  • Controlled reporting solutions.

  • Additional proof-of-concept projects.

Potential Benefits

• Better understanding of student decision-making.

• Identification of unmet program demand.

• Improved enrollment management.

• Strategic planning around program offerings.

DataVista Discussion

Question Raised

• Could this enhanced student journey data eventually be incorporated into DataVista?

Response

• Possible, but would require evaluation and prioritization.

• Existing DataVista infrastructure already incorporates some transfer-related information.

• Team will explore:

  • Similarities between current DataVista metrics and the proposed model.

  • Potential future integration opportunities.

  • Funnel-chart style visualizations.

Related Example

• Alabama's efforts to build a unified K–14 data repository were discussed as an example of statewide longitudinal data integration.

Reporting Environment Upgrade

Overview

The team is upgrading the current reporting environment.

Current State

• Reporting is currently delivered through JasperSoft.

• Existing reports include:

  • LGBTQ reporting

  • CCCApply reporting

  • Fraud reporting

Future State

• Migration to a unified Superset reporting environment.

• Consolidation of reporting tools into a single platform.

• Expanded opportunities for:

  • Standardized reports

  • District-specific reports

  • Research-focused reporting

Request to Members

Participants were asked to provide ideas for:

• New reports.

• Frequently requested analyses.

• Reports that could benefit multiple colleges.

Data Distribution Subscription Service

Overview

The team provided an update on the Data Warehouse Subscription Service.

Purpose

Instead of colleges manually pulling warehouse data:

• Data can be automatically pushed into local environments.

Current Pilot Implementations

• Data delivery to Snowflake environments.

• Data delivery to SQL Server environments.

Benefits

• Reduces manual data extraction.

• Supports recurring research needs.

• Provides automated incremental updates.

• Centralized monitoring and quality control.

Current Participants

• Santiago Community College District

• Contra Costa Community College District

Future Plans

• Formal announcement and documentation.

• Upcoming webinar demonstrations.

• Additional pilot participants encouraged.

Upcoming Webinars and Support Initiatives

Planned Topics

• Data Distribution Subscription Service

• New Superset Reporting Center

• Office hours and drop-in support sessions

Goals

• Improve awareness of warehouse capabilities.

• Provide hands-on support.

• Increase adoption of reporting and data services.

AI for Data Management and Reporting Discussion

Discussion Leader

Terrence

Key Questions Raised

• How should AI be used in data management and reporting?

• Where are opportunities?

• Where are the risks?

Potential AI Uses

• Assisting with SQL development.

• Helping users navigate data documentation.

• Natural-language querying of reporting systems.

• AI-assisted reporting and dashboard interaction.

Concerns Raised

• Student-level data privacy.

• Security risks.

• Overreliance on AI-generated outputs.

• Data governance and accountability.

• Potential misuse by non-technical users.

Industry Comparisons

Participants discussed:

• Financial services

• Healthcare

• Medical imaging

These industries are actively exploring AI while operating under strict privacy regulations.

Victor's Insights from PyCon 2026

• AI-generated code quality remains a major concern.

• Open-source maintainers report increasing volumes of low-quality AI-generated submissions.

• Human review remains essential.

• Responsibility must remain with the human author, even when AI assists.

General Consensus

• AI has useful applications.

• Human oversight remains critical.

• Institutions should begin evaluating policies and guardrails.

• The group should continue monitoring developments in regulated industries.

Closing Remarks

• Continued appreciation for the group's feedback and participation.

• Emphasis on balancing technical possibilities with policy considerations.

• Future discussions will continue around:

  • Reporting modernization

  • Data sharing opportunities

  • AI governance

  • Student journey analytics