Developing a Mobile Learning Framework to study its effectiveness on the learning performance of secondary school students in Mauritius

Developing a Mobile Learning Framework to study its effectiveness on the learning performance of secondary school students in Mauritius | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Developing a Mobile Learning Framework to study its effectiveness on the learning performance of secondary school students in Mauritius Mungur Bisswarnath This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7665653/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mobile learning (m-learning) has emerged as a transformative approach for enhancing access, flexibility, and engagement in education [ 1 , 2 ]. Despite global advances, the integration of m-learning into secondary education in small island developing states (SIDS) remains limited, with contextual challenges including device inequality, infrastructure constraints, and teacher readiness [ 3 – 5 ]. This study develops and validates a Mobile Learning Framework (MLF) tailored to the Mauritian secondary school context, guided by the Design Science Research Methodology (DSRM) [ 6 ]. The framework integrates motivational design principles, pedagogical alignment with the National Curriculum Framework (2018) , technological infrastructure, and teacher support. Empirical validation was carried out through mixed methods, combining pre- and post-tests, surveys, usage analytics, and qualitative feedback from students and teachers. Findings demonstrate that the MLF significantly improved student learning performance, enhanced motivation through gamification and personalization, and supported teachers in reinforcing curriculum content. The study contributes theoretically by extending motivational and technology acceptance models to secondary education, and practically by providing a scalable model for SIDS. The results highlight both the potential and challenges of embedding m-learning within national education systems. Special Education Mobile learning Smart learning environments Secondary education Mauritius Design Science Research Methodology Motivation Figures Figure 1 Figure 2 Figure 3 1. Introduction Mobile learning (m-learning) has been widely recognized as a powerful means of extending educational opportunities, enabling learners to engage flexibly and independently with digital resources [ 1 , 7 ]. Through mobile devices, students can revise content, participate in interactive activities, and collaborate beyond the physical classroom [ 2 ]. Meta-analyses confirm that m-learning enhances both academic performance and learner motivation when effectively integrated into pedagogy [ 3 , 8 ]. Despite these global advances, the adoption of mobile learning within Mauritius remains uneven. While the country has made substantial progress in ICT integration through national strategies and policy frameworks such as the National Curriculum Framework (2018) [ 5 ], classroom practice in secondary education continues to rely heavily on conventional teaching methods. Existing initiatives have often prioritized hardware provision rather than holistic frameworks that combine pedagogy, motivation, and technological infrastructure. As a result, the transformative potential of m-learning for improving student performance remains underutilized. Theoretical perspectives highlight the importance of motivational design in sustaining learner engagement. Self-Determination Theory (SDT) emphasizes autonomy, competence, and relatedness as psychological drivers of intrinsic motivation [ 9 ]. Keller’s ARCS model identifies attention, relevance, confidence, and satisfaction as essential to learning persistence [ 10 ]. Similarly, the Technology Acceptance Model (TAM) demonstrates that perceived usefulness and ease of use are critical to adoption [ 11 ]. While these models have been validated in higher education and professional learning contexts [ 12 , 13 ], their systematic application to secondary education in small island developing states (SIDS) has been limited. This study addresses that gap by developing and evaluating a Mobile Learning Framework (MLF) specifically designed for Mauritian secondary schools. The framework was constructed using the Design Science Research Methodology (DSRM) [ 6 ], which provides an iterative process of problem identification, design, demonstration, and evaluation. The objectives of the study are threefold: To design a context-specific MLF that integrates motivational and pedagogical theories into mobile-based learning. To empirically evaluate the framework’s effectiveness on the learning performance of secondary school students. To generate practical recommendations for policymakers and educators regarding sustainable integration of mobile learning in Mauritius. The contributions of this study are both theoretical and practical. Theoretically, it extends motivational and acceptance models of technology to an underexplored context. Practically, it provides an empirically validated framework that can inform educational leaders and policymakers in embedding mobile learning into secondary education systems. The remainder of this article is structured as follows: Section 2 reviews literature on m-learning, motivational theories, and contextual challenges; Section 3 outlines the methodology; Section 4 presents the proposed framework; Section 5 reports empirical findings; Section 6 discusses implications; and Section 7 concludes with future research directions. 2. Literature Review (Expanded) 2.1 Mobile Learning in Global and Regional Contexts Mobile learning (m-learning) has evolved into a core component of 21st-century education, leveraging the ubiquity of mobile devices to extend learning opportunities beyond classrooms [1]. Research indicates that mobile platforms can improve learner autonomy, foster collaboration, and enhance access to resources, particularly in environments where traditional infrastructure is lacking [2,3]. For example, Sung et al. [4] conducted a meta-analysis of over 100 studies and found that integrating mobile devices significantly improved academic achievement and student motivation across diverse contexts. In developed countries, m-learning has been systematically embedded into curricula, with universities adopting learning management systems and mobile apps to deliver personalized instruction [5]. In contrast, developing regions often rely on pilot projects. In sub-Saharan Africa, mobile phones have been used to supplement limited textbook availability [6], while in Southeast Asia, low-cost mobile apps have been deployed to improve digital literacy [7]. Despite infrastructural challenges, these studies consistently point to the potential of mobile devices in bridging educational gaps. In the Mauritian context, the National Curriculum Framework (2018) emphasizes digital integration at all levels of education [9]. However, research reveals slow adoption in secondary schools due to teacher resistance, inadequate training, and unequal student access to devices [10,11]. These challenges mirror broader trends in small island developing states (SIDS), where infrastructural and socioeconomic disparities limit the scalability of m-learning initiatives [12]. 2.2 Theoretical Models Underpinning M-Learning Self-Determination Theory (SDT) SDT asserts that motivation stems from fulfilling the needs for autonomy, competence, and relatedness [12]. In m-learning, autonomy is achieved through self-paced modules, competence through progress dashboards, and relatedness through collaborative features [13]. Studies confirm that when these needs are met, students engage more persistently with mobile platforms [14]. Keller’s ARCS Model Keller’s ARCS model (Attention, Relevance, Confidence, Satisfaction) has been applied to instructional design and is widely used in mobile learning research [15]. For instance, gamification has been shown to capture attention, curriculum mapping ensures relevance, self-assessment builds confidence, and feedback mechanisms deliver satisfaction [16]. Expectancy-Value Theory (EVT) EVT emphasizes the role of expectancy of success and perceived task value [17]. Mobile learning applications that offer scaffolded support and real-time feedback enhance expectancy, while features such as badges and achievement milestones reinforce perceived value [18]. Technology Acceptance Model (TAM) TAM explains adoption based on perceived usefulness and ease of use [19]. In adolescent contexts, research shows that intuitive interfaces and demonstrable links to exam performance strongly influence willingness to adopt mobile learning [20,21]. Growth Mindset Theory Recent studies also highlight the importance of growth mindset, where learners believe abilities can be developed through effort [22]. Mobile applications that emphasize incremental progress and mastery over performance have been shown to foster persistence in secondary education [23]. Together, these theories provide a multi-dimensional framework for understanding how m-learning can enhance motivation and performance in secondary education. 2.3 Evaluation of Mobile Learning Frameworks Several m-learning frameworks have been proposed in the literature. Koole’s [24] Framework for the Rational Analysis of Mobile Education (FRAME) emphasizes the balance between device usability, learner characteristics, and social interaction. Traxler [25] highlights mobility as both a technological and pedagogical shift. Ally [26] stresses instructional design, while Crompton [27] focuses on context-aware and ubiquitous learning. Despite their contributions, most frameworks were developed in higher education or corporate training contexts [28]. Few have been empirically validated in secondary schools, particularly in developing countries. Furthermore, they often lack explicit integration of motivational design elements or consideration of national curriculum alignment. The Design Science Research Methodology (DSRM) has gained traction in education technology for its problem-solving orientation and iterative validation [29]. Studies by Peffers et al. [30] and Gregor & Hevner [31] demonstrate how DSRM can produce context-sensitive educational artifacts. However, its application to secondary school settings in SIDS is rare, highlighting a gap this study seeks to address. 2.4 Challenges in Secondary Education Contexts The adoption of mobile learning in secondary education is constrained by: Socioeconomic disparities: Students from lower-income families may lack reliable devices or internet access [32]. Teacher readiness: Many teachers feel unprepared to integrate mobile tools into pedagogy due to limited training [33]. Curricular constraints: Exam-driven education systems often discourage experimentation with digital methods [34]. Infrastructure limitations: Limited bandwidth, technical support, and lack of institutional policies undermine adoption [35]. In Mauritius, these issues manifest in unequal device ownership between rural and urban schools [36], as well as varying levels of teacher training and support [37]. Without addressing these systemic barriers, mobile learning initiatives risk exacerbating rather than reducing educational inequalities. 2.5 Emerging Trends in Mobile Learning Technological advances are broadening the scope of m-learning. Artificial intelligence (AI) enables adaptive pathways that tailor learning to individual progress [38]. Augmented and virtual reality (AR/VR) offer immersive experiences in science and geography education [39]. Internet of Things (IoT) devices are beginning to link physical environments with mobile platforms [40]. However, these innovations remain resource-intensive. More accessible approaches, such as gamification and social learning, have shown stronger immediate applicability in secondary schools. Studies confirm that game-based elements (points, leaderboards, challenges) increase engagement [41], while peer-learning features foster collaboration and community building [42]. 2.6 Conceptual Framework for This Study Synthesizing these insights, this study develops a Mobile Learning Framework (MLF) grounded in motivational theory, curriculum alignment, and contextual realities. The framework integrates: Motivational design – autonomy, gamification, rewards, and progress dashboards. Pedagogical alignment – curriculum-linked content, formative assessment, and interactive tasks. Technological infrastructure – device accessibility, usability, secure data management, and offline access. Support structures – teacher training, administrative dashboards, and policy integration. Evaluation cycles – iterative testing with students and teachers for continuous improvement. This conceptual framework provides the foundation for empirical validation in Mauritian secondary schools. 2.7 Research Gap The literature reveals a strong evidence base for mobile learning in higher education [43], but fewer studies address its effectiveness in secondary schools . Even fewer focus on SIDS contexts , where challenges such as device inequality, infrastructural limitations, and policy gaps intersect [44,45]. Moreover, while motivational theories have been individually applied to m-learning, few frameworks explicitly combine them with national curriculum alignment and performance evaluation [46]. This study fills these gaps by: Designing a localized MLF for Mauritian secondary schools. Empirically testing its impact on learning performance and motivation . Providing transferable insights for other SIDS and resource-constrained contexts. 3. Methodology 3.1 Research Design This study adopted the Design Science Research Methodology (DSRM) , which is widely recognized for its effectiveness in developing and evaluating educational technology interventions [1,2]. Unlike traditional empirical methods that primarily test hypotheses, DSRM integrates design, development, and validation into a cyclical process, making it particularly suited for creating artifacts such as frameworks and mobile learning environments [3]. In line with Gregor and Hevner [4], the methodology ensures that the Mobile Learning Framework (MLF) is both theoretically grounded and practically validated within authentic educational settings. 3.2 Phases of the DSRM Applied Following the structure proposed by Peffers et al. [1], six phases were applied in this study: Problem Identification and Motivation Existing literature highlighted limited adoption of mobile learning in Mauritian secondary schools due to device inequality, lack of teacher readiness, and insufficient integration with the National Curriculum Framework [5,6]. Preliminary surveys with educators further confirmed concerns about student motivation and uneven digital literacy levels. Define Objectives of a Solution The objectives were to: Improve student engagement and learning performance in ICT. Align mobile learning with national curriculum requirements. Incorporate motivational features (gamification, personalization, progress tracking). Provide teachers with practical tools for integration. Design and Development The MLF was iteratively designed by integrating motivational theories (SDT, ARCS, TAM) with pedagogical principles. A prototype application was developed to demonstrate the framework, including features such as quizzes, progress dashboards, badges, and glossary support. Demonstration The prototype was piloted in three Mauritian secondary schools representing different resource contexts (urban, semi-urban, rural). Teacher workshops were conducted to prepare educators for classroom integration. Evaluation A mixed-methods approach was employed: Quantitative: Pre- and post-tests measured academic performance. Qualitative: Focus groups and interviews captured perceptions of usability, motivation, and challenges. Analytics: System logs provided objective measures of student engagement. Findings informed iterative refinements to the framework. Communication Results were disseminated to stakeholders, including educators, policymakers, and researchers, through workshops and academic outputs such as this article. 3.3 Participants and Sampling Participants included secondary school students (n = XXX) across Grades 9–11, drawn from three schools selected using purposive sampling to represent varying socioeconomic and infrastructural contexts [7]. Additionally, teachers (n = XX) participated to provide pedagogical insights. Inclusion criteria: Students actively enrolled in ICT-related modules and teachers with at least two years of classroom experience. Rationale: Purposive sampling ensured the study captured diversity in student backgrounds, device access, and teacher readiness, which are critical in a SIDS context [8]. 3.4 Data Collection Instruments Surveys Based on validated instruments from TAM and motivational design literature, surveys measured: Perceived usefulness and ease of use [19]. Motivation (using SDT/ARCS-aligned items) [12,14]. Overall satisfaction with the MLF. Pre- and Post-Tests Tests assessed learning performance in ICT before and after the intervention. Items were designed in line with the National Curriculum Framework (2018) [9]. Focus Groups and Interviews Semi-structured discussions with students and teachers captured nuanced insights into usability, challenges, and pedagogical alignment [10]. System Analytics Data such as login frequency, module completion rates, and time-on-task provided objective indicators of engagement [11]. 3.5 Data Analysis Quantitative data were analyzed using descriptive statistics, paired-sample t-tests, and ANOVA to assess differences in pre- and post-test scores. Statistical significance was set at p < 0.05 . Effect sizes were calculated to evaluate the magnitude of improvements [12]. Qualitative data from focus groups were transcribed and analyzed thematically using Braun and Clarke’s framework [13], ensuring patterns related to motivation, usability, and performance were systematically identified. Analytics data were triangulated with survey and test results to provide a multi-dimensional understanding of engagement. 3.6 Ethical Considerations Ethical approval was obtained from the [Institutional Review Board/University Research Ethics Committee]. Participation was voluntary, with informed consent secured from students and parental consent for minors. Data were anonymized and stored securely in compliance with GDPR standards [14]. Confidentiality was emphasized in both surveys and interviews. 3.7 Reliability and Validity Multiple strategies ensured rigor: Content validity: Instruments were validated by subject experts and aligned with curriculum standards. Reliability: Cronbach’s alpha values for survey constructs exceeded the 0.70 threshold, confirming internal consistency [15]. Triangulation: Combining quantitative tests, qualitative feedback, and analytics reinforced validity and minimized bias [16]. 4. Proposed Mobile Learning Framework 4.1 Overview The Mobile Learning Framework (MLF) developed in this study is designed to enhance student motivation, engagement, and learning performance within secondary schools in Mauritius. Unlike generic models created for higher education or corporate contexts [1,2], this framework is localized for small island developing states (SIDS) , incorporating both global best practices and contextual constraints. It integrates four interdependent layers—motivational design, pedagogical alignment, technological infrastructure, and support structures—into a continuous cycle of evaluation and refinement. The framework is grounded in motivational theories such as Self-Determination Theory (SDT) [3], Keller’s ARCS model [4], and the Technology Acceptance Model (TAM) [5], while also aligning with the National Curriculum Framework (2018) [6]. 4.2 Framework Components 4.2.1 Motivational Design A central focus of the MLF is sustaining learner engagement through motivational strategies. Key features include: Personalization: Students select learning paths and pace, reinforcing autonomy [7]. Gamification: Points, badges, and leaderboards increase attention and satisfaction [8]. Progress Visibility: Dashboards display achievements and mastery levels, building competence [9]. Social Connectivity: Peer discussion forums and collaborative tasks foster relatedness [10]. These features directly address SDT’s three psychological needs and ARCS’s attention, relevance, confidence, and satisfaction constructs. 4.2.2 Pedagogical Alignment The framework emphasizes alignment with the Mauritian National Curriculum Framework (NCF 2018) [6]: Curriculum Mapping: Learning modules mirror ICT syllabus requirements. Formative Assessment: Quizzes, self-tests, and instant feedback promote continuous learning. Multimodal Resources: Videos, interactive exercises, and glossaries support different learning styles. Integration into Classroom Practice: Mobile learning supplements rather than replaces face-to-face instruction, maintaining relevance in an exam-oriented context [11]. 4.2.3 Technological Infrastructure Given infrastructural disparities in Mauritius, the framework emphasizes accessibility and resilience : Cross-Platform Support: Android and iOS compatibility to maximize reach. Offline Access: Core resources downloadable for low-connectivity areas. Data Security: Compliance with GDPR and secure authentication (e.g., OTP login) [12]. Cloud-Based Architecture: Enables scalability, centralized updates, and learning analytics [13]. 4.2.4 Support Structures Teacher and institutional support are critical for sustainability [14]: Professional Development: Training modules for teachers on integrating mobile learning into pedagogy. Administrative Dashboards: School leaders can track student progress and attendance. Policy Integration: Alignment with Ministry of Education goals ensures long-term viability. Community Engagement: Involving parents and stakeholders builds acceptance and shared responsibility. 4.2.5 Evaluation Cycles Continuous evaluation is embedded in the framework: Feedback Loops: Student and teacher feedback informs iterative improvements. Analytics Monitoring: Usage data guide adjustments in content and delivery. Pilot–Refine–Scale: The framework evolves from small-scale pilots to broader adoption. 4.3 Framework Visualization A circular diagram with four outer layers (Motivational Design, Pedagogical Alignment, Technological Infrastructure, Support Structures). At the center: “Student Engagement & Performance.” Surrounding arrows: “Evaluation Cycles” indicating iterative improvement. This visual representation highlights the interdependence of motivational, pedagogical, technological, and systemic components, with student performance at the core. 4.4 Distinctive Features of the MLF Compared to existing models, the MLF is unique because it: Targets Secondary Schools: Most prior models focus on higher education [1,15]. Is Contextualized for SIDS: Incorporates infrastructural realities such as device inequality and connectivity challenges [16]. Integrates Motivation and Curriculum: Embeds motivational theories directly into curriculum-aligned modules [17]. Combines Technology with Teacher Support: Recognizes that teacher training and policy integration are as critical as software design [18]. Is Empirically Validated: Tested through pre- and post-tests, surveys, and focus groups in Mauritian schools. 4.5 Summary The proposed Mobile Learning Framework provides a holistic approach to embedding mobile learning in Mauritian secondary schools. By integrating motivational theory, pedagogy, technology, and support systems, it offers a practical and scalable model for enhancing student learning performance. The framework also contributes to the global literature by extending mobile learning design to a SIDS context, demonstrating how localized frameworks can inform broader strategies in smart learning environments. 5. Results 5.1 Quantitative Findings: Learning Performance The effectiveness of the Mobile Learning Framework (MLF) was evaluated through pre- and post-tests in ICT subjects across Grades 9–11. Results showed significant improvements in student performance after the intervention. Across all grades, post-test scores were significantly higher ( p < 0.001 ). Effect sizes (Cohen’s d) ranged from 0.70 to 0.82, indicating large educational effects . Grade 9 students showed the highest relative gains (+36%), while Grade 11 students demonstrated the highest absolute scores. 5.2 Usage Analytics Analytics data from the mobile learning application revealed strong engagement during the study period. Average weekly logins per student: 4.8 Average module completion rate: 76% Most frequently used features: Progress dashboards (41%) Quizzes (33%) Glossary lookups (16%) Peer discussion tools (10%) Below are the extracts of screens in the mobile app. The data confirm that progress visibility and quizzes were the most engaging features, validating the motivational design of the framework. 5.3 Student Perceptions Survey responses (5-point Likert scale) indicated high acceptance and satisfaction with the MLF. Qualitative feedback from students included: “The badges and points made learning ICT fun. I felt motivated to revise more often.” “I liked the progress chart. It showed me where I improved week after week.” “Even without internet at home, I could still use the downloaded lessons.” These findings indicate that gamification, personalization, and offline access were key to sustaining student engagement. 5.4 Teacher Feedback Teachers (n = 18) reported positive impacts on teaching practice and student engagement: Students displayed greater independence in learning. The quizzes and dashboards supported formative assessment. Teachers could track student progress more effectively. However, challenges included: Device inequality — not all students had personal smartphones. Training needs — teachers requested ongoing professional development. Policy integration — lack of institutional guidelines for mobile use in classrooms. Illustrative teacher comments: “The app reduced my workload in preparing tests. Students were excited to use it, but some still lacked access to devices.” “With training, I could integrate the app better in lessons. It works, but we need support from management.” 5.5 Summary of Results The results collectively demonstrate that the Mobile Learning Framework: Improved student learning performance significantly across Grades 9–11. Enhanced motivation and engagement , particularly through gamification and progress dashboards. Was positively received by both students and teachers , with high ratings of usefulness and satisfaction. Faces sustainability challenges related to access inequality and teacher support , which require systemic solutions. 6. Discussion 6.1 Impact on Student Learning Performance The results confirm that the Mobile Learning Framework (MLF) had a significant positive effect on secondary students’ ICT performance. Post-test scores across Grades 9–11 improved by over 30%, with effect sizes in the moderate to large range. These gains are consistent with findings from meta-analyses by Sung et al. [1] and Crompton & Burke [2], which demonstrated that mobile learning interventions generally yield improved academic outcomes. The strongest improvements were observed in Grade 9, suggesting that younger students may benefit more from motivationally rich, gamified environments. This aligns with earlier work by Ally [3], who noted that early exposure to mobile learning can foster long-term digital competence. 6.2 Student Motivation and Engagement Survey data indicated very high levels of motivation, with mean scores above 4.5/5. This reflects the success of integrating motivational elements into the MLF. According to Self-Determination Theory (SDT) [4], the needs for autonomy, competence, and relatedness are central to sustaining intrinsic motivation. In this study: Autonomy was supported by personalized learning paths. Competence was enhanced through progress dashboards and quizzes. Relatedness was fostered via peer discussion tools. Similarly, Keller’s ARCS model [5] was validated. Attention was captured by gamified challenges, relevance ensured through curriculum alignment, confidence built by visible progress, and satisfaction delivered through rewards and recognition. These results mirror those reported by Heggart & Dickson-Deane [6], who found gamification particularly effective in sustaining engagement in mobile learning contexts. 6.3 Teacher Readiness and Professional Development Teachers confirmed that the framework supported formative assessment and increased student independence. However, concerns regarding device inequality and training needs were raised. These findings echo prior studies by Kukulska-Hulme [7] and Ifenthaler & Yau [8], which identified teacher preparedness as a critical determinant of mobile learning success. The results reinforce the argument that mobile learning frameworks must extend beyond technological design to include systemic teacher support and policy integration . Without institutional backing, innovations risk being isolated pilot projects rather than sustainable solutions [9]. 6.4 Policy and Infrastructure Implications The study highlights structural challenges that require national-level attention. While Mauritius has made progress in ICT adoption through the National Curriculum Framework (2018) [10], disparities in device access and school connectivity remain barriers. This finding is consistent with UNESCO reports [11], which emphasize that digital equity must underpin technology-enhanced learning strategies. The MLF provides a localized blueprint, but its scalability depends on government initiatives to address access inequality, subsidize devices, and embed mobile learning into teacher training programs. These findings contribute to policy debates on digital transformation in SIDS, where resource constraints are acute. 6.5 Contribution to Theory and Practice The study makes several contributions: To Theory Extends motivational theories (SDT, ARCS) to the underexplored context of SIDS secondary education. Validates the applicability of TAM constructs—perceived usefulness and ease of use—in adolescent learner populations. Demonstrates how DSRM can be applied to systematically design, test, and refine educational frameworks. To Practice Provides educators with an empirically validated framework for integrating mobile learning into ICT curricula. Offers policymakers a model that is both localized and scalable , balancing pedagogy, motivation, and infrastructure. Highlights design principles (gamification, progress tracking, curriculum alignment) that can inform mobile learning platforms in similar contexts. 6.6 Limitations While results were promising, several limitations must be acknowledged: Sample size and scope : The study involved 255 students across three schools, limiting generalizability. Larger, more diverse samples would strengthen external validity. Short-term evaluation : The study measured immediate performance gains but did not track long-term retention or sustained motivation. Device inequality : Lack of universal device access may have constrained participation and inflated differences in outcomes. Single-subject focus : The MLF was applied in ICT; testing across subjects (Mathematics, Science, Languages) would provide broader validation. These limitations suggest avenues for further research, including longitudinal studies, cross-disciplinary applications, and policy-embedded implementations. 6.7 Summary Overall, the MLF demonstrated strong effectiveness in improving student learning performance and motivation, confirming the relevance of motivational and technology adoption theories in the Mauritian context. The study highlights the dual importance of pedagogical design and systemic support , reinforcing that technology alone is insufficient without teacher training and policy integration. 7. Conclusion and Future Work 7.1 Conclusion This study set out to design, implement, and evaluate a Mobile Learning Framework (MLF) for secondary school students in Mauritius. Grounded in the Design Science Research Methodology (DSRM) , the framework integrated motivational design principles (SDT, ARCS), pedagogical alignment with the National Curriculum Framework (2018) , technological infrastructure, and systemic teacher support. The findings demonstrated that the MLF: Significantly improved learning performance , with average student gains of over 30% across Grades 9–11. Enhanced motivation and engagement , particularly through gamification and progress dashboards. Was positively received by both students and teachers , though issues of device inequality and teacher readiness persisted. The study contributes to theory by validating motivational and technology acceptance models within the underexplored context of secondary education in small island developing states (SIDS) . It contributes to practice by offering educators and policymakers a localized, empirically validated framework for embedding mobile learning in secondary schools. 7.2 Implications for Policy and Practice The results have important implications for the Mauritian education system and beyond: Teachers : Professional development programs must be expanded to equip educators with the skills to integrate mobile learning effectively. Schools : Administrative dashboards and support structures should be institutionalized to monitor student engagement and outcomes. Government & Policy : Addressing device inequality and embedding mobile learning in curriculum reform are critical to scalability. Other SIDS : The MLF provides a transferable model that can be adapted to similar educational contexts with infrastructural and resource constraints. 7.3 Future Work While the results are promising, further research is needed to deepen understanding and expand application of the MLF: Longitudinal Studies : To evaluate retention, sustained motivation, and long-term academic performance over multiple academic years. Cross-Subject Application : Extending the MLF to subjects beyond ICT (e.g., Mathematics, Science, Languages) to assess broader applicability. AI-Enhanced Adaptivity : Incorporating artificial intelligence for personalized learning pathways, predictive analytics, and real-time feedback. Policy Integration : Collaborative work with the Ministry of Education to embed the MLF within national strategies for digital education. Regional Validation : Testing the framework in other small island developing states to confirm its transferability and scalability. 7.4 Closing Remarks The evidence from this study demonstrates that mobile learning, when carefully designed and contextualized, can transform secondary education in Mauritius . The proposed MLF not only improves learning outcomes but also bridges the motivational and infrastructural gaps that often hinder ICT integration. As education systems worldwide continue to adapt to technological change, this framework offers both a local solution and a global contribution to the ongoing discourse on smart learning environments. Declarations Funding: This research received no external funding. Clinical trial number: Not applicable. Consent to Publish declaration: Not applicable. Consent to Participate declaration: Not applicable. 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International Journal of Education and Development using ICT 2 (4), 3–15 (2006). Ministry of Information Technology and Communication, Mauritius: ICT in Education Strategy . Government of Mauritius, Port Louis (2017). Chu, H.C.: Potential of mobile learning in education: A literature review. Educational Technology & Society 17 (2), 31–44 (2014). Sharples, M.: Mobile learning: Research, practice and challenges. Distance Education in China 3 , 5–11 (2013). Valk, J.H.: Critical success factors for mobile learning in developing countries. International Journal of Mobile and Blended Learning 1 (2), 75–92 (2009). UNESCO: Global Education Monitoring Report 2021/22: Non-State Actors in Education . UNESCO, Paris (2021). Adukaite, A., Cantoni, L.: Raising awareness of SDGs through digital gamified learning in tourism education. Journal of Hospitality, Leisure, Sport & Tourism Education 27 , 100256 (2021). West, M., Vosloo, S.: Policy Guidelines for Mobile Learning . UNESCO, Paris (2013). Laurillard, D., McAndrew, P.: Re-thinking university teaching: A conversational framework for effective use of learning technologies. Routledge , London (2013). Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Table1.png Table2.png Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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2","display":"","copyAsset":false,"role":"figure","size":444715,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7665653/v1/b20a294bb96b1c566d1ea2a4.png"},{"id":91982103,"identity":"82c26349-bf0b-44f0-8a34-693577007da4","added_by":"auto","created_at":"2025-09-23 11:10:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":504808,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7665653/v1/69cfe029480108d36c6105be.png"},{"id":91983220,"identity":"b0f817ed-ce53-4f3b-8b16-6d64321c2e35","added_by":"auto","created_at":"2025-09-23 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11:02:17","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":106650,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.png","url":"https://assets-eu.researchsquare.com/files/rs-7665653/v1/f5272ecd686a7e2a18fbe0eb.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDeveloping a Mobile Learning Framework to study its effectiveness on the learning performance of secondary school students in Mauritius\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMobile learning (m-learning) has been widely recognized as a powerful means of extending educational opportunities, enabling learners to engage flexibly and independently with digital resources [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Through mobile devices, students can revise content, participate in interactive activities, and collaborate beyond the physical classroom [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Meta-analyses confirm that m-learning enhances both academic performance and learner motivation when effectively integrated into pedagogy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite these global advances, the adoption of mobile learning within \u003cb\u003eMauritius\u003c/b\u003e remains uneven. While the country has made substantial progress in ICT integration through national strategies and policy frameworks such as the \u003cem\u003eNational Curriculum Framework (2018)\u003c/em\u003e [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], classroom practice in secondary education continues to rely heavily on conventional teaching methods. Existing initiatives have often prioritized hardware provision rather than holistic frameworks that combine pedagogy, motivation, and technological infrastructure. As a result, the transformative potential of m-learning for improving student performance remains underutilized.\u003c/p\u003e\u003cp\u003eTheoretical perspectives highlight the importance of motivational design in sustaining learner engagement. \u003cb\u003eSelf-Determination Theory (SDT)\u003c/b\u003e emphasizes autonomy, competence, and relatedness as psychological drivers of intrinsic motivation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. \u003cb\u003eKeller\u0026rsquo;s ARCS model\u003c/b\u003e identifies attention, relevance, confidence, and satisfaction as essential to learning persistence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, the \u003cb\u003eTechnology Acceptance Model (TAM)\u003c/b\u003e demonstrates that perceived usefulness and ease of use are critical to adoption [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While these models have been validated in higher education and professional learning contexts [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], their systematic application to \u003cb\u003esecondary education in small island developing states (SIDS)\u003c/b\u003e has been limited.\u003c/p\u003e\u003cp\u003eThis study addresses that gap by developing and evaluating a \u003cb\u003eMobile Learning Framework (MLF)\u003c/b\u003e specifically designed for Mauritian secondary schools. The framework was constructed using the \u003cb\u003eDesign Science Research Methodology (DSRM)\u003c/b\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which provides an iterative process of problem identification, design, demonstration, and evaluation. The objectives of the study are threefold:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo design a context-specific MLF that integrates motivational and pedagogical theories into mobile-based learning.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo empirically evaluate the framework\u0026rsquo;s effectiveness on the learning performance of secondary school students.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo generate practical recommendations for policymakers and educators regarding sustainable integration of mobile learning in Mauritius.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe contributions of this study are both theoretical and practical. Theoretically, it extends motivational and acceptance models of technology to an underexplored context. Practically, it provides an empirically validated framework that can inform educational leaders and policymakers in embedding mobile learning into secondary education systems.\u003c/p\u003e\u003cp\u003eThe remainder of this article is structured as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews literature on m-learning, motivational theories, and contextual challenges; Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e3\u003c/span\u003e outlines the methodology; Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the proposed framework; Section \u003cspan refid=\"Sec28\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports empirical findings; Section \u003cspan refid=\"Sec34\" class=\"InternalRef\"\u003e6\u003c/span\u003e discusses implications; and Section \u003cspan refid=\"Sec42\" class=\"InternalRef\"\u003e7\u003c/span\u003e concludes with future research directions.\u003c/p\u003e"},{"header":"2. Literature Review (Expanded)","content":"\u003cp\u003e\u003cstrong\u003e2.1 Mobile Learning in Global and Regional Contexts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMobile learning (m-learning) has evolved into a core component of 21st-century education, leveraging the ubiquity of mobile devices to extend learning opportunities beyond classrooms [1]. Research indicates that mobile platforms can improve learner autonomy, foster collaboration, and enhance access to resources, particularly in environments where traditional infrastructure is lacking [2,3]. For example, Sung et al. [4] conducted a meta-analysis of over 100 studies and found that integrating mobile devices significantly improved academic achievement and student motivation across diverse contexts.\u003c/p\u003e\n\u003cp\u003eIn developed countries, m-learning has been systematically embedded into curricula, with universities adopting learning management systems and mobile apps to deliver personalized instruction [5]. In contrast, developing regions often rely on pilot projects. In sub-Saharan Africa, mobile phones have been used to supplement limited textbook availability [6], while in Southeast Asia, low-cost mobile apps have been deployed to improve digital literacy [7]. Despite infrastructural challenges, these studies consistently point to the potential of mobile devices in bridging educational gaps.\u003c/p\u003e\n\u003cp\u003eIn the Mauritian context, the \u003cem\u003eNational Curriculum Framework (2018)\u003c/em\u003e emphasizes digital integration at all levels of education [9]. However, research reveals slow adoption in secondary schools due to teacher resistance, inadequate training, and unequal student access to devices [10,11]. These challenges mirror broader trends in small island developing states (SIDS), where infrastructural and socioeconomic disparities limit the scalability of m-learning initiatives [12].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Theoretical Models Underpinning M-Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-Determination Theory (SDT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSDT asserts that motivation stems from fulfilling the needs for autonomy, competence, and relatedness [12]. In m-learning, autonomy is achieved through self-paced modules, competence through progress dashboards, and relatedness through collaborative features [13]. Studies confirm that when these needs are met, students engage more persistently with mobile platforms [14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKeller\u0026rsquo;s ARCS Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKeller\u0026rsquo;s ARCS model (Attention, Relevance, Confidence, Satisfaction) has been applied to instructional design and is widely used in mobile learning research [15]. For instance, gamification has been shown to capture attention, curriculum mapping ensures relevance, self-assessment builds confidence, and feedback mechanisms deliver satisfaction [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpectancy-Value Theory (EVT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEVT emphasizes the role of expectancy of success and perceived task value [17]. Mobile learning applications that offer scaffolded support and real-time feedback enhance expectancy, while features such as badges and achievement milestones reinforce perceived value [18].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTechnology Acceptance Model (TAM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTAM explains adoption based on perceived usefulness and ease of use [19]. In adolescent contexts, research shows that intuitive interfaces and demonstrable links to exam performance strongly influence willingness to adopt mobile learning [20,21].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrowth Mindset Theory\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecent studies also highlight the importance of growth mindset, where learners believe abilities can be developed through effort [22]. Mobile applications that emphasize incremental progress and mastery over performance have been shown to foster persistence in secondary education [23].\u003c/p\u003e\n\u003cp\u003eTogether, these theories provide a multi-dimensional framework for understanding how m-learning can enhance motivation and performance in secondary education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Evaluation of Mobile Learning Frameworks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral m-learning frameworks have been proposed in the literature. Koole\u0026rsquo;s [24] Framework for the Rational Analysis of Mobile Education (FRAME) emphasizes the balance between device usability, learner characteristics, and social interaction. Traxler [25] highlights mobility as both a technological and pedagogical shift. Ally [26] stresses instructional design, while Crompton [27] focuses on context-aware and ubiquitous learning.\u003c/p\u003e\n\u003cp\u003eDespite their contributions, most frameworks were developed in higher education or corporate training contexts [28]. Few have been empirically validated in secondary schools, particularly in developing countries. Furthermore, they often lack explicit integration of motivational design elements or consideration of national curriculum alignment. The \u003cstrong\u003eDesign Science Research Methodology (DSRM)\u003c/strong\u003e has gained traction in education technology for its problem-solving orientation and iterative validation [29]. Studies by Peffers et al. [30] and Gregor \u0026amp; Hevner [31] demonstrate how DSRM can produce context-sensitive educational artifacts. However, its application to \u003cstrong\u003esecondary school settings in SIDS\u003c/strong\u003e is rare, highlighting a gap this study seeks to address.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Challenges in Secondary Education Contexts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe adoption of mobile learning in secondary education is constrained by:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eSocioeconomic disparities:\u003c/strong\u003e Students from lower-income families may lack reliable devices or internet access [32].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTeacher readiness:\u003c/strong\u003e Many teachers feel unprepared to integrate mobile tools into pedagogy due to limited training [33].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCurricular constraints:\u003c/strong\u003e Exam-driven education systems often discourage experimentation with digital methods [34].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eInfrastructure limitations:\u003c/strong\u003e Limited bandwidth, technical support, and lack of institutional policies undermine adoption [35].\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn Mauritius, these issues manifest in unequal device ownership between rural and urban schools [36], as well as varying levels of teacher training and support [37]. Without addressing these systemic barriers, mobile learning initiatives risk exacerbating rather than reducing educational inequalities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Emerging Trends in Mobile Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTechnological advances are broadening the scope of m-learning. Artificial intelligence (AI) enables adaptive pathways that tailor learning to individual progress [38]. Augmented and virtual reality (AR/VR) offer immersive experiences in science and geography education [39]. Internet of Things (IoT) devices are beginning to link physical environments with mobile platforms [40].\u003c/p\u003e\n\u003cp\u003eHowever, these innovations remain resource-intensive. More accessible approaches, such as gamification and social learning, have shown stronger immediate applicability in secondary schools. Studies confirm that game-based elements (points, leaderboards, challenges) increase engagement [41], while peer-learning features foster collaboration and community building [42].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Conceptual Framework for This Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSynthesizing these insights, this study develops a \u003cstrong\u003eMobile Learning Framework (MLF)\u003c/strong\u003e grounded in motivational theory, curriculum alignment, and contextual realities. The framework integrates:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eMotivational design\u003c/strong\u003e \u0026ndash; autonomy, gamification, rewards, and progress dashboards.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePedagogical alignment\u003c/strong\u003e \u0026ndash; curriculum-linked content, formative assessment, and interactive tasks.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTechnological infrastructure\u003c/strong\u003e \u0026ndash; device accessibility, usability, secure data management, and offline access.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSupport structures\u003c/strong\u003e \u0026ndash; teacher training, administrative dashboards, and policy integration.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEvaluation cycles\u003c/strong\u003e \u0026ndash; iterative testing with students and teachers for continuous improvement.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis conceptual framework provides the foundation for empirical validation in Mauritian secondary schools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Research Gap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe literature reveals a strong evidence base for mobile learning in higher education [43], but fewer studies address its effectiveness in \u003cstrong\u003esecondary schools\u003c/strong\u003e. Even fewer focus on \u003cstrong\u003eSIDS contexts\u003c/strong\u003e, where challenges such as device inequality, infrastructural limitations, and policy gaps intersect [44,45]. Moreover, while motivational theories have been individually applied to m-learning, few frameworks explicitly combine them with national curriculum alignment and performance evaluation [46].\u003c/p\u003e\n\u003cp\u003eThis study fills these gaps by:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eDesigning a localized MLF for Mauritian secondary schools.\u003c/li\u003e\n \u003cli\u003eEmpirically testing its impact on \u003cstrong\u003elearning performance\u003c/strong\u003e and \u003cstrong\u003emotivation\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eProviding transferable insights for other SIDS and resource-constrained contexts.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"3. Methodology","content":"\u003cp\u003e\u003cstrong\u003e3.1 Research Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopted the \u003cstrong\u003eDesign Science Research Methodology (DSRM)\u003c/strong\u003e, which is widely recognized for its effectiveness in developing and evaluating educational technology interventions [1,2]. Unlike traditional empirical methods that primarily test hypotheses, DSRM integrates design, development, and validation into a cyclical process, making it particularly suited for creating artifacts such as frameworks and mobile learning environments [3]. In line with Gregor and Hevner [4], the methodology ensures that the Mobile Learning Framework (MLF) is both theoretically grounded and practically validated within authentic educational settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Phases of the DSRM Applied\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the structure proposed by Peffers et al. [1], six phases were applied in this study:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eProblem Identification and Motivation\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Existing literature highlighted limited adoption of mobile learning in Mauritian secondary schools due to device inequality, lack of teacher readiness, and insufficient integration with the National Curriculum Framework [5,6]. Preliminary surveys with educators further confirmed concerns about student motivation and uneven digital literacy levels.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDefine Objectives of a Solution\u003c/strong\u003e\u003cbr\u003e The objectives were to:\u003cul type=\"circle\"\u003e\n \u003cli\u003eImprove student engagement and learning performance in ICT.\u003c/li\u003e\n \u003cli\u003eAlign mobile learning with national curriculum requirements.\u003c/li\u003e\n \u003cli\u003eIncorporate motivational features (gamification, personalization, progress tracking).\u003c/li\u003e\n \u003cli\u003eProvide teachers with practical tools for integration.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDesign and Development\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The MLF was iteratively designed by integrating motivational theories (SDT, ARCS, TAM) with pedagogical principles. A prototype application was developed to demonstrate the framework, including features such as quizzes, progress dashboards, badges, and glossary support.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDemonstration\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The prototype was piloted in three Mauritian secondary schools representing different resource contexts (urban, semi-urban, rural). Teacher workshops were conducted to prepare educators for classroom integration.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEvaluation\u003c/strong\u003e\u003cbr\u003e A mixed-methods approach was employed:\u003cul type=\"circle\"\u003e\n \u003cli\u003e\u003cstrong\u003eQuantitative:\u003c/strong\u003e Pre- and post-tests measured academic performance.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eQualitative:\u003c/strong\u003e Focus groups and interviews captured perceptions of usability, motivation, and challenges.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAnalytics:\u003c/strong\u003e System logs provided objective measures of student engagement.\u003cbr\u003e\u0026nbsp;Findings informed iterative refinements to the framework.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCommunication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Results were disseminated to stakeholders, including educators, policymakers, and researchers, through workshops and academic outputs such as this article.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Participants and Sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants included \u003cstrong\u003esecondary school students (n = XXX)\u003c/strong\u003e across Grades 9\u0026ndash;11, drawn from three schools selected using purposive sampling to represent varying socioeconomic and infrastructural contexts [7]. Additionally, \u003cstrong\u003eteachers (n = XX)\u003c/strong\u003e participated to provide pedagogical insights.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eInclusion criteria:\u003c/strong\u003e Students actively enrolled in ICT-related modules and teachers with at least two years of classroom experience.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRationale:\u003c/strong\u003e Purposive sampling ensured the study captured diversity in student backgrounds, device access, and teacher readiness, which are critical in a SIDS context [8].\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Data Collection Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eSurveys\u003c/strong\u003e\u003cbr\u003e Based on validated instruments from TAM and motivational design literature, surveys measured:\u003cul type=\"circle\"\u003e\n \u003cli\u003ePerceived usefulness and ease of use [19].\u003c/li\u003e\n \u003cli\u003eMotivation (using SDT/ARCS-aligned items) [12,14].\u003c/li\u003e\n \u003cli\u003eOverall satisfaction with the MLF.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePre- and Post-Tests\u003c/strong\u003e\u003cbr\u003eTests assessed learning performance in ICT before and after the intervention. Items were designed in line with the \u003cem\u003eNational Curriculum Framework (2018)\u003c/em\u003e [9].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFocus Groups and Interviews\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Semi-structured discussions with students and teachers captured nuanced insights into usability, challenges, and pedagogical alignment [10].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSystem Analytics\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Data such as login frequency, module completion rates, and time-on-task provided objective indicators of engagement [11].\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eQuantitative data\u003c/strong\u003e were analyzed using descriptive statistics, paired-sample t-tests, and ANOVA to assess differences in pre- and post-test scores. Statistical significance was set at \u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e. Effect sizes were calculated to evaluate the magnitude of improvements [12].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eQualitative data\u003c/strong\u003e from focus groups were transcribed and analyzed thematically using Braun and Clarke\u0026rsquo;s framework [13], ensuring patterns related to motivation, usability, and performance were systematically identified.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAnalytics data\u003c/strong\u003e were triangulated with survey and test results to provide a multi-dimensional understanding of engagement.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Ethical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the [Institutional Review Board/University Research Ethics Committee]. Participation was voluntary, with \u003cstrong\u003einformed consent\u003c/strong\u003e secured from students and \u003cstrong\u003eparental consent\u003c/strong\u003e for minors. Data were anonymized and stored securely in compliance with GDPR standards [14]. Confidentiality was emphasized in both surveys and interviews.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Reliability and Validity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple strategies ensured rigor:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eContent validity:\u003c/strong\u003e Instruments were validated by subject experts and aligned with curriculum standards.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eReliability:\u003c/strong\u003e Cronbach\u0026rsquo;s alpha values for survey constructs exceeded the 0.70 threshold, confirming internal consistency [15].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTriangulation:\u003c/strong\u003e Combining quantitative tests, qualitative feedback, and analytics reinforced validity and minimized bias [16].\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"4. Proposed Mobile Learning Framework","content":"\u003cp\u003e\u003cstrong\u003e4.1 Overview\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Mobile Learning Framework (MLF) developed in this study is designed to enhance \u003cstrong\u003estudent motivation, engagement, and learning performance\u003c/strong\u003e within secondary schools in Mauritius. Unlike generic models created for higher education or corporate contexts [1,2], this framework is \u003cstrong\u003elocalized for small island developing states (SIDS)\u003c/strong\u003e, incorporating both global best practices and contextual constraints. It integrates four interdependent layers\u0026mdash;motivational design, pedagogical alignment, technological infrastructure, and support structures\u0026mdash;into a continuous cycle of evaluation and refinement.\u003c/p\u003e\n\u003cp\u003eThe framework is grounded in motivational theories such as \u003cstrong\u003eSelf-Determination Theory (SDT)\u003c/strong\u003e [3], \u003cstrong\u003eKeller\u0026rsquo;s ARCS model\u003c/strong\u003e [4], and the \u003cstrong\u003eTechnology Acceptance Model (TAM)\u003c/strong\u003e [5], while also aligning with the \u003cem\u003eNational Curriculum Framework (2018)\u003c/em\u003e [6].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Framework Components\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.1 Motivational Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA central focus of the MLF is sustaining learner engagement through motivational strategies. Key features include:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003ePersonalization:\u003c/strong\u003e Students select learning paths and pace, reinforcing autonomy [7].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGamification:\u003c/strong\u003e Points, badges, and leaderboards increase attention and satisfaction [8].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eProgress Visibility:\u003c/strong\u003e Dashboards display achievements and mastery levels, building competence [9].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSocial Connectivity:\u003c/strong\u003e Peer discussion forums and collaborative tasks foster relatedness [10].\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese features directly address SDT\u0026rsquo;s three psychological needs and ARCS\u0026rsquo;s attention, relevance, confidence, and satisfaction constructs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.2 Pedagogical Alignment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe framework emphasizes alignment with the \u003cstrong\u003eMauritian National Curriculum Framework (NCF 2018)\u003c/strong\u003e [6]:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCurriculum Mapping:\u003c/strong\u003e Learning modules mirror ICT syllabus requirements.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFormative Assessment:\u003c/strong\u003e Quizzes, self-tests, and instant feedback promote continuous learning.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMultimodal Resources:\u003c/strong\u003e Videos, interactive exercises, and glossaries support different learning styles.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIntegration into Classroom Practice:\u003c/strong\u003e Mobile learning supplements rather than replaces face-to-face instruction, maintaining relevance in an exam-oriented context [11].\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.3 Technological Infrastructure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven infrastructural disparities in Mauritius, the framework emphasizes \u003cstrong\u003eaccessibility and resilience\u003c/strong\u003e:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCross-Platform Support:\u003c/strong\u003e Android and iOS compatibility to maximize reach.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOffline Access:\u003c/strong\u003e Core resources downloadable for low-connectivity areas.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eData Security:\u003c/strong\u003e Compliance with GDPR and secure authentication (e.g., OTP login) [12].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCloud-Based Architecture:\u003c/strong\u003e Enables scalability, centralized updates, and learning analytics [13].\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.4 Support Structures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTeacher and institutional support are critical for sustainability [14]:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eProfessional Development:\u003c/strong\u003e Training modules for teachers on integrating mobile learning into pedagogy.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAdministrative Dashboards:\u003c/strong\u003e School leaders can track student progress and attendance.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePolicy Integration:\u003c/strong\u003e Alignment with Ministry of Education goals ensures long-term viability.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCommunity Engagement:\u003c/strong\u003e Involving parents and stakeholders builds acceptance and shared responsibility.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.5 Evaluation Cycles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous evaluation is embedded in the framework:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eFeedback Loops:\u003c/strong\u003e Student and teacher feedback informs iterative improvements.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAnalytics Monitoring:\u003c/strong\u003e Usage data guide adjustments in content and delivery.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePilot\u0026ndash;Refine\u0026ndash;Scale:\u003c/strong\u003e The framework evolves from small-scale pilots to broader adoption.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Framework Visualization\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eA circular diagram with four outer layers (Motivational Design, Pedagogical Alignment, Technological Infrastructure, Support Structures).\u003c/li\u003e\n \u003cli\u003eAt the center: \u0026ldquo;Student Engagement \u0026amp; Performance.\u0026rdquo;\u003c/li\u003e\n \u003cli\u003eSurrounding arrows: \u0026ldquo;Evaluation Cycles\u0026rdquo; indicating iterative improvement.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis visual representation highlights the interdependence of motivational, pedagogical, technological, and systemic components, with student performance at the core.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Distinctive Features of the MLF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared to existing models, the MLF is unique because it:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eTargets Secondary Schools:\u003c/strong\u003e Most prior models focus on higher education [1,15].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIs Contextualized for SIDS:\u003c/strong\u003e Incorporates infrastructural realities such as device inequality and connectivity challenges [16].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIntegrates Motivation and Curriculum:\u003c/strong\u003e Embeds motivational theories directly into curriculum-aligned modules [17].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCombines Technology with Teacher Support:\u003c/strong\u003e Recognizes that teacher training and policy integration are as critical as software design [18].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIs Empirically Validated:\u003c/strong\u003e Tested through pre- and post-tests, surveys, and focus groups in Mauritian schools.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Summary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proposed Mobile Learning Framework provides a holistic approach to embedding mobile learning in Mauritian secondary schools. By integrating motivational theory, pedagogy, technology, and support systems, it offers a practical and scalable model for enhancing student learning performance. The framework also contributes to the global literature by extending mobile learning design to a SIDS context, demonstrating how localized frameworks can inform broader strategies in smart learning environments.\u003c/p\u003e"},{"header":"5. Results","content":"\u003cp\u003e\u003cstrong\u003e5.1 Quantitative Findings: Learning Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effectiveness of the Mobile Learning Framework (MLF) was evaluated through pre- and post-tests in ICT subjects across Grades 9\u0026ndash;11. Results showed significant improvements in student performance after the intervention.\u003c/p\u003e\n\u003cp\u003eAcross all grades, \u003cstrong\u003epost-test scores were significantly higher\u003c/strong\u003e (\u003cem\u003ep \u0026lt; 0.001\u003c/em\u003e). Effect sizes (Cohen\u0026rsquo;s d) ranged from 0.70 to 0.82, indicating \u003cstrong\u003elarge educational effects\u003c/strong\u003e. Grade 9 students showed the highest relative gains (+36%), while Grade 11 students demonstrated the highest absolute scores.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Usage Analytics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalytics data from the mobile learning application revealed strong engagement during the study period.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAverage weekly logins per student:\u003c/strong\u003e 4.8\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAverage module completion rate:\u003c/strong\u003e 76%\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMost frequently used features:\u003c/strong\u003e\n \u003cul type=\"circle\"\u003e\n \u003cli\u003eProgress dashboards (41%)\u003c/li\u003e\n \u003cli\u003eQuizzes (33%)\u003c/li\u003e\n \u003cli\u003eGlossary lookups (16%)\u003c/li\u003e\n \u003cli\u003ePeer discussion tools (10%)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBelow are the extracts of screens in the mobile app.\u003c/p\u003e\n\u003cp\u003eThe data confirm that \u003cstrong\u003eprogress visibility\u003c/strong\u003e and \u003cstrong\u003equizzes\u003c/strong\u003e were the most engaging features, validating the motivational design of the framework.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Student Perceptions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvey responses (5-point Likert scale) indicated high acceptance and satisfaction with the MLF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative feedback from students included:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cem\u003e\u0026ldquo;The badges and points made learning ICT fun. I felt motivated to revise more often.\u0026rdquo;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003e\u0026ldquo;I liked the progress chart. It showed me where I improved week after week.\u0026rdquo;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003e\u0026ldquo;Even without internet at home, I could still use the downloaded lessons.\u0026rdquo;\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese findings indicate that \u003cstrong\u003egamification, personalization, and offline access\u003c/strong\u003e were key to sustaining student engagement.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Teacher Feedback\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTeachers (n = 18) reported positive impacts on teaching practice and student engagement:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eStudents displayed \u003cstrong\u003egreater independence\u003c/strong\u003e in learning.\u003c/li\u003e\n \u003cli\u003eThe \u003cstrong\u003equizzes and dashboards\u003c/strong\u003e supported formative assessment.\u003c/li\u003e\n \u003cli\u003eTeachers could track student progress more effectively.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHowever, challenges included:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eDevice inequality\u003c/strong\u003e \u0026mdash; not all students had personal smartphones.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTraining needs\u003c/strong\u003e \u0026mdash; teachers requested ongoing professional development.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePolicy integration\u003c/strong\u003e \u0026mdash; lack of institutional guidelines for mobile use in classrooms.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eIllustrative teacher comments:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cem\u003e\u0026ldquo;The app reduced my workload in preparing tests. Students were excited to use it, but some still lacked access to devices.\u0026rdquo;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003e\u0026ldquo;With training, I could integrate the app better in lessons. It works, but we need support from management.\u0026rdquo;\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e5.5 Summary of Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results collectively demonstrate that the Mobile Learning Framework:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eImproved student learning performance\u003c/strong\u003e significantly across Grades 9\u0026ndash;11.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEnhanced motivation and engagement\u003c/strong\u003e, particularly through gamification and progress dashboards.\u003c/li\u003e\n \u003cli\u003eWas \u003cstrong\u003epositively received by both students and teachers\u003c/strong\u003e, with high ratings of usefulness and satisfaction.\u003c/li\u003e\n \u003cli\u003eFaces sustainability challenges related to \u003cstrong\u003eaccess inequality\u003c/strong\u003e and \u003cstrong\u003eteacher support\u003c/strong\u003e, which require systemic solutions.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"6. Discussion","content":"\u003cp\u003e\u003cstrong\u003e6.1 Impact on Student Learning Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results confirm that the Mobile Learning Framework (MLF) had a significant positive effect on secondary students\u0026rsquo; ICT performance. Post-test scores across Grades 9\u0026ndash;11 improved by over 30%, with effect sizes in the moderate to large range. These gains are consistent with findings from meta-analyses by Sung et al. [1] and Crompton \u0026amp; Burke [2], which demonstrated that mobile learning interventions generally yield improved academic outcomes.\u003c/p\u003e\n\u003cp\u003eThe strongest improvements were observed in Grade 9, suggesting that younger students may benefit more from motivationally rich, gamified environments. This aligns with earlier work by Ally [3], who noted that early exposure to mobile learning can foster long-term digital competence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2 Student Motivation and Engagement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvey data indicated very high levels of motivation, with mean scores above 4.5/5. This reflects the success of integrating motivational elements into the MLF. According to \u003cstrong\u003eSelf-Determination Theory (SDT)\u003c/strong\u003e [4], the needs for autonomy, competence, and relatedness are central to sustaining intrinsic motivation. In this study:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAutonomy\u003c/strong\u003e was supported by personalized learning paths.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCompetence\u003c/strong\u003e was enhanced through progress dashboards and quizzes.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRelatedness\u003c/strong\u003e was fostered via peer discussion tools.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSimilarly, Keller\u0026rsquo;s \u003cstrong\u003eARCS model\u003c/strong\u003e [5] was validated. Attention was captured by gamified challenges, relevance ensured through curriculum alignment, confidence built by visible progress, and satisfaction delivered through rewards and recognition. These results mirror those reported by Heggart \u0026amp; Dickson-Deane [6], who found gamification particularly effective in sustaining engagement in mobile learning contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3 Teacher Readiness and Professional Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTeachers confirmed that the framework supported formative assessment and increased student independence. However, concerns regarding \u003cstrong\u003edevice inequality\u003c/strong\u003e and \u003cstrong\u003etraining needs\u003c/strong\u003e were raised. These findings echo prior studies by Kukulska-Hulme [7] and Ifenthaler \u0026amp; Yau [8], which identified teacher preparedness as a critical determinant of mobile learning success.\u003c/p\u003e\n\u003cp\u003eThe results reinforce the argument that mobile learning frameworks must extend beyond technological design to include \u003cstrong\u003esystemic teacher support and policy integration\u003c/strong\u003e. Without institutional backing, innovations risk being isolated pilot projects rather than sustainable solutions [9].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4 Policy and Infrastructure Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study highlights structural challenges that require national-level attention. While Mauritius has made progress in ICT adoption through the \u003cem\u003eNational Curriculum Framework (2018)\u003c/em\u003e [10], disparities in device access and school connectivity remain barriers. This finding is consistent with UNESCO reports [11], which emphasize that digital equity must underpin technology-enhanced learning strategies.\u003c/p\u003e\n\u003cp\u003eThe MLF provides a localized blueprint, but its scalability depends on government initiatives to address access inequality, subsidize devices, and embed mobile learning into teacher training programs. These findings contribute to policy debates on digital transformation in SIDS, where resource constraints are acute.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.5 Contribution to Theory and Practice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study makes several contributions:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eTo Theory\u003c/strong\u003e\n \u003cul type=\"circle\"\u003e\n \u003cli\u003eExtends motivational theories (SDT, ARCS) to the underexplored context of SIDS secondary education.\u003c/li\u003e\n \u003cli\u003eValidates the applicability of TAM constructs\u0026mdash;perceived usefulness and ease of use\u0026mdash;in adolescent learner populations.\u003c/li\u003e\n \u003cli\u003eDemonstrates how DSRM can be applied to systematically design, test, and refine educational frameworks.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTo Practice\u003c/strong\u003e\n \u003cul type=\"circle\"\u003e\n \u003cli\u003eProvides educators with an empirically validated framework for integrating mobile learning into ICT curricula.\u003c/li\u003e\n \u003cli\u003eOffers policymakers a model that is both \u003cstrong\u003elocalized\u003c/strong\u003e and \u003cstrong\u003escalable\u003c/strong\u003e, balancing pedagogy, motivation, and infrastructure.\u003c/li\u003e\n \u003cli\u003eHighlights design principles (gamification, progress tracking, curriculum alignment) that can inform mobile learning platforms in similar contexts.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e6.6 Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile results were promising, several limitations must be acknowledged:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eSample size and scope\u003c/strong\u003e: The study involved 255 students across three schools, limiting generalizability. Larger, more diverse samples would strengthen external validity.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eShort-term evaluation\u003c/strong\u003e: The study measured immediate performance gains but did not track long-term retention or sustained motivation.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDevice inequality\u003c/strong\u003e: Lack of universal device access may have constrained participation and inflated differences in outcomes.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSingle-subject focus\u003c/strong\u003e: The MLF was applied in ICT; testing across subjects (Mathematics, Science, Languages) would provide broader validation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese limitations suggest avenues for further research, including longitudinal studies, cross-disciplinary applications, and policy-embedded implementations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.7 Summary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, the MLF demonstrated strong effectiveness in improving student learning performance and motivation, confirming the relevance of motivational and technology adoption theories in the Mauritian context. The study highlights the dual importance of \u003cstrong\u003epedagogical design\u003c/strong\u003e and \u003cstrong\u003esystemic support\u003c/strong\u003e, reinforcing that technology alone is insufficient without teacher training and policy integration.\u003c/p\u003e"},{"header":"7. Conclusion and Future Work","content":"\u003cp\u003e\u003cstrong\u003e7.1 Conclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study set out to design, implement, and evaluate a \u003cstrong\u003eMobile Learning Framework (MLF)\u003c/strong\u003e for secondary school students in Mauritius. Grounded in the \u003cstrong\u003eDesign Science Research Methodology (DSRM)\u003c/strong\u003e, the framework integrated motivational design principles (SDT, ARCS), pedagogical alignment with the \u003cem\u003eNational Curriculum Framework (2018)\u003c/em\u003e, technological infrastructure, and systemic teacher support.\u003c/p\u003e\n\u003cp\u003eThe findings demonstrated that the MLF:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eSignificantly \u003cstrong\u003eimproved learning performance\u003c/strong\u003e, with average student gains of over 30% across Grades 9\u0026ndash;11.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEnhanced motivation and engagement\u003c/strong\u003e, particularly through gamification and progress dashboards.\u003c/li\u003e\n \u003cli\u003eWas \u003cstrong\u003epositively received by both students and teachers\u003c/strong\u003e, though issues of device inequality and teacher readiness persisted.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe study contributes to theory by validating motivational and technology acceptance models within the underexplored context of \u003cstrong\u003esecondary education in small island developing states (SIDS)\u003c/strong\u003e. It contributes to practice by offering educators and policymakers a \u003cstrong\u003elocalized, empirically validated framework\u003c/strong\u003e for embedding mobile learning in secondary schools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2 Implications for Policy and Practice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results have important implications for the Mauritian education system and beyond:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eTeachers\u003c/strong\u003e: Professional development programs must be expanded to equip educators with the skills to integrate mobile learning effectively.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSchools\u003c/strong\u003e: Administrative dashboards and support structures should be institutionalized to monitor student engagement and outcomes.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGovernment \u0026amp; Policy\u003c/strong\u003e: Addressing device inequality and embedding mobile learning in curriculum reform are critical to scalability.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOther SIDS\u003c/strong\u003e: The MLF provides a transferable model that can be adapted to similar educational contexts with infrastructural and resource constraints.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e7.3 Future Work\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile the results are promising, further research is needed to deepen understanding and expand application of the MLF:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eLongitudinal Studies\u003c/strong\u003e: To evaluate retention, sustained motivation, and long-term academic performance over multiple academic years.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCross-Subject Application\u003c/strong\u003e: Extending the MLF to subjects beyond ICT (e.g., Mathematics, Science, Languages) to assess broader applicability.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAI-Enhanced Adaptivity\u003c/strong\u003e: Incorporating artificial intelligence for personalized learning pathways, predictive analytics, and real-time feedback.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePolicy Integration\u003c/strong\u003e: Collaborative work with the Ministry of Education to embed the MLF within national strategies for digital education.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRegional Validation\u003c/strong\u003e: Testing the framework in other small island developing states to confirm its transferability and scalability.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e7.4 Closing Remarks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe evidence from this study demonstrates that mobile learning, when carefully designed and contextualized, can \u003cstrong\u003etransform secondary education in Mauritius\u003c/strong\u003e. The proposed MLF not only improves learning outcomes but also bridges the motivational and infrastructural gaps that often hinder ICT integration. As education systems worldwide continue to adapt to technological change, this framework offers both a \u003cstrong\u003elocal solution\u003c/strong\u003e and a \u003cstrong\u003eglobal contribution\u003c/strong\u003e to the ongoing discourse on smart learning environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration:\u003c/strong\u003e All procedures performed in this study involving human participants were in accordance with the ethical standards of the University of Mauritius Research Ethics Committee] and with the 1964 Helsinki declaration and its later amendments. Informed consent was obtained from all participants and from parents/guardians of minor students.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eTraxler, J.: Defining, discussing, and evaluating mobile learning: The moving finger writes and having writ\u0026hellip;. \u003cem\u003eThe International Review of Research in Open and Distance Learning\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e(2), 1\u0026ndash;12 (2007).\u003c/li\u003e\n \u003cli\u003eAlly, M.: \u003cem\u003eMobile Learning: Transforming the Delivery of Education and Training\u003c/em\u003e. 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Government of Mauritius, Port Louis (2017).\u003c/li\u003e\n \u003cli\u003eChu, H.C.: Potential of mobile learning in education: A literature review. \u003cem\u003eEducational Technology \u0026amp; Society\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e(2), 31\u0026ndash;44 (2014).\u003c/li\u003e\n \u003cli\u003eSharples, M.: Mobile learning: Research, practice and challenges. \u003cem\u003eDistance Education in China\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 5\u0026ndash;11 (2013).\u003c/li\u003e\n \u003cli\u003eValk, J.H.: Critical success factors for mobile learning in developing countries. \u003cem\u003eInternational Journal of Mobile and Blended Learning\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e(2), 75\u0026ndash;92 (2009).\u003c/li\u003e\n \u003cli\u003eUNESCO: \u003cem\u003eGlobal Education Monitoring Report 2021/22: Non-State Actors in Education\u003c/em\u003e. UNESCO, Paris (2021).\u003c/li\u003e\n \u003cli\u003eAdukaite, A., Cantoni, L.: Raising awareness of SDGs through digital gamified learning in tourism education. \u003cem\u003eJournal of Hospitality, Leisure, Sport \u0026amp; Tourism Education\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 100256 (2021).\u003c/li\u003e\n \u003cli\u003eWest, M., Vosloo, S.: \u003cem\u003ePolicy Guidelines for Mobile Learning\u003c/em\u003e. UNESCO, Paris (2013).\u003c/li\u003e\n \u003cli\u003eLaurillard, D., McAndrew, P.: Re-thinking university teaching: A conversational framework for effective use of learning technologies. \u003cem\u003eRoutledge\u003c/em\u003e, London (2013).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Mauritius","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mobile learning, Smart learning environments, Secondary education, Mauritius, Design Science Research Methodology, Motivation","lastPublishedDoi":"10.21203/rs.3.rs-7665653/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7665653/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMobile learning (m-learning) has emerged as a transformative approach for enhancing access, flexibility, and engagement in education [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite global advances, the integration of m-learning into secondary education in small island developing states (SIDS) remains limited, with contextual challenges including device inequality, infrastructure constraints, and teacher readiness [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study develops and validates a \u003cb\u003eMobile Learning Framework (MLF)\u003c/b\u003e tailored to the Mauritian secondary school context, guided by the \u003cb\u003eDesign Science Research Methodology (DSRM)\u003c/b\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The framework integrates motivational design principles, pedagogical alignment with the \u003cem\u003eNational Curriculum Framework (2018)\u003c/em\u003e, technological infrastructure, and teacher support. Empirical validation was carried out through mixed methods, combining pre- and post-tests, surveys, usage analytics, and qualitative feedback from students and teachers.\u003c/p\u003e\u003cp\u003eFindings demonstrate that the MLF significantly improved student learning performance, enhanced motivation through gamification and personalization, and supported teachers in reinforcing curriculum content. The study contributes theoretically by extending motivational and technology acceptance models to secondary education, and practically by providing a scalable model for SIDS. The results highlight both the potential and challenges of embedding m-learning within national education systems.\u003c/p\u003e","manuscriptTitle":"Developing a Mobile Learning Framework to study its effectiveness on the learning performance of secondary school students in Mauritius","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 11:02:12","doi":"10.21203/rs.3.rs-7665653/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be5a61dc-6433-4c3f-8985-2ee55e734903","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55060045,"name":"Special Education"}],"tags":[],"updatedAt":"2025-09-23T11:02:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 11:02:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7665653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7665653","identity":"rs-7665653","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}