THE TIME-ORIENTED LEARNER: A FRAMEWORK FOR DESIGNING PERSONALIZED MOBILE INTERFACES TO ENHANCE TECHNOLOGY ACCEPTANCE AND SELF-EFFICACY, AND REDUCE COGNITIVE LOAD

Mobile learning (m-learning) design often relies on a one-size-fits-all, universal approach. This approach may create cognitive friction for some learners. This occurs when their inherent time orientation, a stable trait governing how individuals manage tasks and time, is misaligned with the interface's structure. This study introduces and evaluates Time-Oriented Personalized Interfaces (TOPI), a novel approach that aligns interface design with learners' monochronic, polychronic, or neutral dispositions. We investigated the effects of this temporal alignment on university students' technology acceptance, self-efficacy, and cognitive load. A sequential explanatory mixed-methods design was employed with 150 university students. In the quantitative phase, monochronic and polychronic learners were assigned to interact with either a personalized interface matched to their time orientation or a universal interface serving as a control. Neutral learners were assigned to the universal interface to assess its baseline effectiveness. Pre- and post-tests were used to measure the key variables. Subsequently, semi-structured interviews with 30 participants provided qualitative insights into their experiences. Quantitative results revealed that, for both monochronic and polychronic learners, using a temporally aligned interface led to significantly higher technology acceptance and self-efficacy, as well as significantly lower cognitive load, compared to the universal baseline. Qualitative analysis confirmed these findings, showing monochronic learners valued structured, sequential workflows while polychronic learners thrived in flexible, multitasking environments. The universal interface was found to be effective and cognitively manageable for neutral learners. This study makes a significant contribution by establishing time orientation as a critical, empirically validated dimension for personalization in m-learning. Theoretically, it extends Person-Environment Fit theory to the design of digital learning environments. Practically, it provides a novel framework (TOPI) with actionable principles that challenge the dominance of one-size-fits-all design by demonstrating a clear path toward creating more effective, adaptive, and user-centered learning systems. This study provides compelling evidence that while a generic interface may be adequate for neutral learners, it is suboptimal for those with stronger temporal dispositions. Designing adaptive interfaces that accommodate temporal diversity is a key strategy for enhancing learning quality beyond mere usability, offering a practical framework for creating more effective and engaging digital learning environments.