The research, published in Discover Education by Rajesh Moza and Varun Nayyar of Chitkara University, surveyed 434 undergraduate and postgraduate students enrolled in online and distance education programmes in India. Using a sophisticated statistical technique known as partial least squares structural equation modelling, or PLS-SEM, the authors tested a mechanism-based model linking technology-enabled learning to what students perceive as their learning outcomes. The results were striking. Technology-enabled learning showed no direct shortcut to perceived learning gains. Rather, its influence travelled indirectly through flexible learning environments, which in turn boosted learner motivation and self-regulated learning, ultimately enhancing the outcomes students reported.

The numbers behind this pathway are remarkable. Technology-enabled learning was positively associated with flexible learning with a path coefficient of 0.862, one of the strongest relationships in the entire model. Flexible learning, in turn, was linked to motivation with a coefficient of 0.660 and to self-regulated learning with a coefficient of 0.737. Finally, motivation was associated with perceived learning outcomes at 0.265, while self-regulated learning showed an even stronger association at 0.578. The model explained 74.4 percent of the variance in flexible learning, 43.6 percent of the variance in motivation, 54.2 percent of the variance in self-regulated learning, and 62.3 percent of the variance in perceived learning outcomes—explanatory power that the authors describe as moderate to substantial.

What makes this study particularly important is its theoretical grounding. The authors combined transactional distance theory, which holds that pedagogical distance in online environments is shaped by dialogue, structure, and learner independence, with flexibility theory, which emphasises that adaptive learning environments support diverse learner needs and pathways. From this perspective, technology-enabled learning reduces transactional distance by increasing learner control and interaction opportunities. But the crucial insight is that flexibility does not arise automatically from online delivery. Similar technologies can produce either rigid or flexible learning experiences depending on how institutions and instructors embed technological affordances into course structures and pedagogical strategies.

Consider a concrete example drawn from the study’s discussion. A university may deliver lectures online, yet still require students to attend a designated proctored examination location rather than allowing them to take the test from home. Or an institution may use technology to deliver content while maintaining fixed schedules and rigid deadlines. In both cases, the technological infrastructure exists, but the pedagogical and institutional choices determine whether learners actually experience flexibility. This distinction between technology-enabled learning as an enabling condition and flexible learning as a pedagogical process lies at the heart of the study’s contribution, explaining why earlier evaluations of educational technology have reported such inconsistent results.

The mediating analysis revealed that the total indirect effect of technology-enabled learning on perceived learning outcomes was 0.518, with the self-regulated learning pathway contributing 0.367 and the motivation pathway contributing 0.151. The self-regulation pathway was substantially stronger than the motivation pathway, suggesting that behavioural learning strategies—planning, monitoring, and adapting one’s own learning activities—play a more decisive role than motivational drive alone in achieving perceived learning gains. This finding aligns with decades of research on self-regulated learning, including foundational work showing that learner autonomy and self-management are hallmarks of successful online students.

Motivation still mattered, however. The study drew on self-determination theory, which differentiates between intrinsic motivation arising from personal interest and inherent satisfaction, and extrinsic motivation driven by external rewards such as career advancement. Flexible learning environments allow students to choose when, where, and how they engage with learning tools, strengthening intrinsic motivation by satisfying the psychological need for autonomy. Adult learners and working professionals in the sample showed a blend of both motivational types, pursuing personal growth alongside professional recognition and promotion opportunities. Motivated learners persisted in challenging tasks and perceived their learning journeys more positively, even though the motivational pathway was statistically weaker than the self-regulatory one.

The methodology deserves attention for its rigour. All measurement constructs demonstrated strong reliability, with Cronbach’s alpha values ranging from 0.883 to 0.927 and composite reliability values from 0.919 to 0.942, well above the recommended threshold of 0.70. Average variance extracted ranged from 0.697 to 0.746, confirming convergent validity, and all indicator loadings exceeded 0.70. Discriminant validity was assessed using the heterotrait-monotrait ratio, with most construct pairs falling below the 0.90 threshold. Two pairs—technology-enabled learning with flexible learning at 0.936, and flexible learning with perceived learning outcomes at 0.916—marginally exceeded the threshold, which the authors attribute to the close conceptual proximity of these constructs in technology-supported ecosystems. They caution that from the learner’s perspective, the empirical distinction between technological infrastructure and the flexibility it affords may be less pronounced than the theoretical distinction suggests, since flexibility is frequently achieved through technology-enabled platforms.

The practical implications are significant for institutions, instructional designers, and policymakers, particularly in developing countries where online education addresses barriers related to geography, employment, and infrastructure. The study suggests that simply adopting advanced platforms or tools will not improve perceived learning outcomes unless those technologies are deliberately used to support learner control over time, pace, and access. Instructional designers should prioritise asynchronous participation, modular content, revisitable learning materials, and self-paced modules. Faculty development initiatives should emphasise pedagogical design for flexibility rather than technical training alone. Policymakers may consider flexible learning as a strategic component of national online education frameworks, especially where learners balance academic responsibilities with work and family commitments.

The authors are careful to note the limitations of their cross-sectional design. Because data were collected at a single point in time, the significant direct and indirect associations identified by the model should not be interpreted as definitive evidence of causality. The use of self-reported survey data may introduce common method bias, and the convenience sampling method focused on Indian higher education students may restrict generalisability. The study also measured perceived learning outcomes rather than objectively assessed academic achievement, reflecting students’ perceptions of learning rather than independently verified performance. Future research employing longitudinal designs, experimental manipulations of specific design attributes, and objective learning performance measures would be valuable for establishing causal and temporal relationships. Nevertheless, by repositioning flexible learning from a descriptive feature of online education to a theoretically significant pedagogical mechanism, this study offers a process-oriented explanation that clarifies how and why technology-enabled learning contributes to perceived learning outcomes in higher education—and why the presence of technology alone has never been sufficient.

Subject of Research: Education Research

Article Title: Technology-enabled learning and perceived learning outcomes in online higher education

Article References: Moza, R., & Nayyar, V. (2026). Technology-enabled learning and perceived learning outcomes in online higher education. Discover Education, 5(1), Article 1183. https://doi.org/10.1007/s44217-026-02254-7

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02254-7

Keywords: online learning, educational technology, flexible learning, self-regulated learning, higher education, PLS-SEM, motivation, India, Technology-enabled, learning, perceived, outcomes