2026 ASEE Annual Conference & Exposition

Online education has become a key modality for expanding access, flexibility, and lifelong learning opportunities in higher education, particularly among adult learners and working professionals. Its rapid growth has opened new pathways for professional advancement, but it has also raised challenges regarding the perceived quality and effectiveness of teaching and learning processes. In this context, a School of Engineering in South America has implemented fully online programs under two distinct formats: Advance, designed for professionals with prior work experience seeking to complete their undergraduate degrees through flexible pathways and recognition of prior learning; and 100% Online Evening Programs, both grounded in a student-centered instructional design model focused on quality assurance and continuous improvement. Despite these advancements, existing literature highlights that perceived quality in online higher education depends on factors such as instructional design, student–instructor interaction, academic support, and students’ digital competence. Therefore, this study aims to analyze students’ perceptions of quality in online engineering programs, identify key influencing factors, and propose actionable recommendations to strengthen the learning experience and institutional management practices. This study follows a quantitative, descriptive, non-experimental, cross-sectional design. A non-probabilistic sample of 161 engineering students enrolled in online programs participated in the study. The study employed the Online Learning Experience instrument, designed to assess the factors influencing students’ adoption, engagement, and perceived quality of online higher education. The instrument builds upon the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, integrating constructs from the Technology Acceptance Model (TAM) and the Quality Matters Framework for higher education to capture a comprehensive view of the online learning experience. It comprises 48 items across ten dimensions: Performance Expectancy (PE), Effort Expectancy (EE), Attitude Toward Online Learning (ATT), Social Influence (SI), Facilitating Conditions (FC), Behavioral Intention (BI), Habit (HT), Online Learning Value (OLV), Satisfaction (S), and Quality (Q), measured on a five-point Likert scale. The Quality dimension, informed by instructional quality models and the Quality Matters standards, includes indicators such as student engagement, alignment between objectives and activities, timely feedback, and instructor–student interaction. In this research, particular emphasis is placed on the Quality dimension, as the study aims to analyze how instructional design, academic support, and technological factors shape students’ perceived quality of online programs. Data analysis was conducted using Excel, Power BI, and JASP, incorporating demographic, academic, and professional variables. Preliminary results suggest a generally positive perception of flexibility and accessibility in online learning, while highlighting potential areas for improvement in feedback practices, personalized learning, and social interaction. The perceived quality of online programs is expected to depend most strongly on instructor presence, timely feedback, and the reliability of technological infrastructure. Limited access or inconsistent interaction can diminish students’ perception of quality even when course content meets academic standards. Students who perceive higher Effort Expectancy (ease of use) and stronger Facilitating Conditions (technical and institutional support) are expected to report higher perceptions of quality in online programs. The findings of this study provide empirical evidence and a replicable diagnostic framework for assessing quality in online engineering education from the learner’s perspective. Moreover, they contribute to the broader field of continuing and professional education by offering insights that guide institutional policy, enhance instructional design, and ensure excellence, relevance, and satisfaction in virtual learning environments for engineering professionals.