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The Resource Gadgets Pedagogical Framework: A conceptual model for lecturer-guided mobile learning in
1School of Health Sciences, Mama Ngina University College, Kenya.
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Mobile learning (m-learning) is education delivered anytime, anywhere via pocket-sized, internet-connected devices. M-learning enables nursing students to access clinical guidelines, multimedia resources, and evidence-based knowledge. However, in many low- and middle-income countries (LMICs), institutional simulation laboratories, audiovisual equipment, and WiFi remain limited, constraining the implementation of technology-enhanced pedagogy. Paradoxically, smartphone ownership among students is widespread, presenting an underutilized opportunity for structured mobile-assisted learning. This paper presents the Resource Gadgets Pedagogical Framework (RGPF), a conceptual model for lecturer-guided smartphone-mediated learning in resource-constrained nursing education. The framework is empirically informed by qualitative data from undergraduate nursing students and theoretically grounded in six complementary learning theories: constructivism, critical pedagogy, andragogy, Cognitive Theory of Multimedia Learning, Activity Theory, and Self-Determination Theory. The RGPF conceptualizes mobile-assisted learning as a structured five-step instructional sequence: (1) Structured Initiation (Frame task), (2) Guided Digital Inquiry (Specify sources), (3) Visual-Multimodal Processing (Time-bound search), (4) Collaborative Synthesis, and (5) Educator-Facilitated Anchoring (Anchor learning). Each component is theoretically grounded and operationalized through practical classroom strategies without institutional technological investment. The Resource Gadgets Pedagogical Framework (RGPF) contributes a replicable pedagogical model that reframes students' smartphones from perceived classroom distractions to structured cognitive learning tools. The framework embeds digital equity safeguards as prerequisite implementation conditions for lecturer-guided mobile-assisted learning and positions m-learning as a gateway toward AI-enhanced nursing education. While analytically generalizable, the framework requires multi-site empirical validation. The RGPF offers educators, curriculum designers, and policymakers in LMICs a foundation for scalable, equitable, and theory-driven innovation in nursing education across resource-constrained settings.
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