Application of Multilevel Parameter Algorithm to Enhance College Admission and Referral System

The admissions processes in Philippine Higher Education Institutions (HEIs) often rely on rigid, score-based criteria that struggle to adapt to unfilled program slots, applicant withdrawals, and equality requirements. These limitations make implementing a consistent referral procedure difficult, compromising fairness and increasing the administrative burden. This study aimed to develop and implement an automated admission and referral system based on the Multilevel Parameter Algorithm (MLPA) to manage prioritization, tie-breaking, and multi-stage referrals, while ensuring adherence to program capacity and policy guidelines. Unlike prior models that remain theoretical, simulation-based, or limited to recommendation stages, this study demonstrates a fully deployed MLPA-based admission and referral system. System quality evaluation by 10 Information and Communications Technology (ICT) professionals using ISO/IEC 25010 indicates excellent ratings across all criteria (overall M = 4.72, SD = 0.06). Meanwhile, functionality testing by 18 admission and staff personnel likewise unanimously validated the operational correctness of the system’s core functions. From the applicant side, the system’s usability was evaluated by 1287 first-year students from across colleges using the expanded Post-Study System Usability Questionnaire (PSSUQ), which yielded an overall usability of 2.02 (Good Usability), well above the PSSUQ Norm3 benchmark. The result likewise indicates that 255 (17.48%) of the respondents were successfully referred to their second-choice programs. The study demonstrates that an MLPA-based system can uphold fairness and user satisfaction from admission personnel and applicants, offering a replicable model for HEIs seeking to modernize admissions through transparent, data-driven, and equity-oriented digital solutions.