The paper introduces D-CODE, a new framework blending Data Colony Optimization (DCO) algorithms inspired by biological colonies' collective behaviors with Dynamic Efficiency (DE) models for real-time adaptation. DCO utilizes metaheuristic strategies from ant colonies, bee swarms, and fungal networks to efficiently explore complex data landscapes, while DE enables continuous resource recalibration and process adjustments for optimal performance amidst changing conditions. Through a mixedmethods approach involving simulations and case studies, D-CODE outperforms traditional techniques, showing improvements of 3-4% in solution quality, 2-3 times faster convergence rates, and up to 25% higher computational efficiency. The integration of DCO's robust optimization and DE's dynamic responsiveness positions D-CODE as a transformative paradigm for intelligent systems design, with potential applications in operational efficiency, decision support, and computational intelligence, supported by empirical validation and promising outcomes.

Before following any teaching technique, it is very important to Learn the Learner and then to implement the teaching technique based on the learner's capability. Hence, I propose Learner-centered Teaching methodology [LCTM] or Student-Centric Teaching Technique [SCTT].