Date of Award

6-9-2025

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2025

Department

Computer Science and Engineering

First Advisor

Shiva Jahangiri

Abstract

In recent years, the gap between traditional education and the current labor market has widened dramatically: in 2024, 11. 5 % of California’s 16-24 year olds were neither in school nor employed, alongside a net decline of roughly 3 million students nationwide in the last decade and more than 4 million members of Generation Z left the job despite having conventional degrees. These trends underscore a growing disconnect between academic credentials and market-ready skills and a pressing need for alternative pathways to gainful work. We introduce SIDEQUE$T, a secure, user-friendly online marketplace engineered to bridge this divide. By allowing job seekers to tag and prioritize their core competencies, SIDEQUE$T’s recommendation engine delivers highly tailored task and gig opportunities—no formal degree required. Our platform vets and categorizes postings, matches them to each user’s skill profile, and continually refines recommendations via feedback and performance data. This paper presents SIDEQUE$T’s system architecture, recommendation algorithm, and user-experience design, and evaluates its effectiveness through early adoption metrics and user satisfaction surveys. By reimagining how emerging talent connects with opportunity, SIDEQUE$T offers a scalable solution to today’s “worthless degrees” dilemma and paves the way for a more inclusive, skills-driven economy.

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