# Influence of Admission Pathways on Learning Strategies, Assessment Engagement, and Academic Performance Among First-Year Medical Students: Mixed Methods Retrospective Observational and Cross-Sectional Survey Study

## TL;DR

This study shows how different ways students get into medical school affect their learning habits, exam performance, and stress levels.

## Contribution

This is the first study to analyze how admission pathways influence learning strategies, assessment engagement, and academic performance together in a single cohort.

## Key findings

- Students admitted via academic pathways showed higher academic performance and lower stress compared to others.
- Rural pathway students had lower academic scores but more exam attempts, suggesting a need for targeted support.
- Summative scores were positively linked to learning outcomes and number of exam attempts, but negatively to first-pass attempts.

## Abstract

Medical school admission pathways are designed to select suitable applicants, with different approaches potentially impacting students’ learning behaviors and performance.

This study aimed to compare students’ self-regulated learning (SRL) strategies, assessment engagement statistics (AES), nongrading evaluation (Outstanding [“O”]/Satisfactory [“S”]/Unsatisfactory [“U”]) preferences, and academic performance across admission pathways, and analyze correlations and linear regression models among summative scores, AES, and course learning outcome (CLO) scores.

This mixed methods retrospective observational and cross-sectional survey study used census sampling with selection criteria of all enrolled first-year medical students in 2021 (N=319) across 4 admission pathways: academic (n=23), quota (n=6), test (n=261), and rural (n=29). Demographics included age (19‐24 years) and sex (167/319, 52.4% male). AES, CLO scores, and summative scores were obtained from institutional databases. Two system-embedded institutional questionnaires assessed SRL strategies (316/319, 99.1% response rate) and “O”/“S”/“U” preferences (299/319, 93.7% response rate). Outcome measures included SRL strategies, AES, “O”/“S”/“U” preferences, CLO scores, and summative scores. Statistical significance was set at P<.05.

When compared among pathways, using one-way ANOVA with Fisher least significant difference post hoc tests, the academic group reported significantly higher mean (with 95% CI) goal setting (4.35, 4.07‐4.63), enthusiasm (4.43, 4.18‐4.69), and lower stress during study (2.64, 2.15‐3.12), while the rural group showed higher pre-examination stress (4.38, 4.10‐4.66) (all P<.05). Most academic (14/22, 63.6%), quota (5/6, 83.3%), and test students (132/243, 54.3%) preferred “O”/ “S”/“U,” while the rural students preferred “S”/“U” (13/28, 46.4%). The academic group showed significantly higher CLO and summative scores but fewer total and intentional attempts and instances of first-pass and highest scoring attempts (all P<.05), whereas the rural group showed significantly lower CLO and summative scores and higher instances of first-pass and highest scoring attempts (all P<.05). For correlation analyses, using Pearson correlation coefficient, summative scores were positively correlated with CLO scores and number of passings and negatively with first-pass attempts. For multiple linear regression analyses, summative scores were positively influenced by number of passings for each CLO and CLO scores and negatively influenced by instances of first-pass attempts and highest scoring attempts. Overall, the academic group demonstrated higher academic performance and fewer attempts and instances of first-pass and highest scoring attempts, while the rural group showed lower academic performance, requiring more attempts for first-passing CLOs.

Admission pathways significantly influence students’ SRL strategies, AES, evaluation preferences, and academic performance. This study is innovative in analyzing these interconnected components within a single cohort, unlike prior research that examined them separately. By integrating assessment-engagement analytics with SRL data, it offers equity-oriented evidence on how admission systems shape learning behaviors and academic trajectories. These findings provide actionable insights for inclusive curriculum design and early identification of at-risk students. Real-world implications include targeted mentoring, SRL-focused interventions, and assessment reforms balancing academic rigor with psychological safety.