Efficiency-Centered Strategy Generation in Elementary Algorithm Education: An Instructional Design Framework Informed by Algorithm Engineering and Data-Driven Algorithm Design

Foundational algorithm instruction at the elementary level has centered on understanding control structures and reproducing given procedures; opportunities for learners to generate strategies and judge efficiency under varying data conditions remain limited. This article presents an instructional design framework for elementary algorithm education that adapts selected principles of Algorithm Engineering and Data-Driven Algorithm Design, with algorithmic complexity as a cross-cutting criterion, and reports how a formative expert evaluation informed its refinement. The framework was derived through a focused reconstruction of artifacts from a design and development study: principles were selected using three criteria, traceably mapped to design requirements, and the expert evaluation was re-examined. The reconstruction yielded five design requirements, six design principles, five operating principles, and a four-phase model: Recognizing Problems in Context, Exploring Analytical Solution Strategies, Implementing Condition-Responsive Procedures, and Reflective Evaluation and Optimization; formal proof and asymptotic analysis lie outside its scope. Nine of ten expert-rated items met the original study’s content validity criterion, and expert feedback led to concrete learner-level activities, collaboration across all phases, and delayed presentation of canonical algorithms. The contribution is an explicit, traceable account of how professional practice principles were selected, bounded, adapted, and organized for efficiency-centered elementary algorithm instruction.