Analyze proposal text quality and clarity.
Future work could examine how writing style, structure, and sentiment in the
cleaned_essays, cleaned_titles, and cleaned_summary fields influence approval outcomes.
This would help distinguish strong proposals from weaker ones beyond cost and scale alone.
Study time-to-approval across project types.
If submission and approval timestamps become available in the future,
measuring how long different projects take to get approved could reveal additional bottlenecks,
especially for high-cost projects and underfunded subject categories.
Incorporate school- and location-level context using available data.
Using the existing school_state field, future analysis could explore whether approval patterns
differ systematically across states and whether some regions consistently experience lower success rates.
Build early-warning models for at-risk projects.
Predictive models using project cost, quantity, subject categories, grade level,
teacher experience, and text features from the proposal could help flag projects with a low
probability of approval and provide real-time feedback to teachers before submission.
Evaluate the impact of targeted support and visibility strategies.
If targeted support or visibility campaigns are introduced for specific subjects or regions,
future analysis could assess whether these interventions successfully reduce approval disparities
across subject categories and experience levels.