Data-Driven Instructional Decision-Making: Framework, Instrument, and Process

Abstract

Instructional decision-making is the process exploring, judging, and selecting instructional implementation options to effectively achieve instructional goals. However, traditional instructional decision-making relies excessively on subjective experience. The insufficient information hinders teachers comprehensively and accurately identifying the existing problems. Educational big data provides a scientific basis for the formulation, implementation, and optimization of teaching strategies. This study elaborates on the concept, importance, and development of instructional decision-making. It explores the design of data-driven instructional decision-making based on three aspects: framework, instrument, and process. The exploration promotes the transformation of instructional decision-making from experience-driven to data-driven and provides solutions to personalized and precise instructional decision-making, so as to enhance teaching quality and students' learning outcomes.