人机协同与循证决策融合的《决策理论与方法》课程教学改革研究

Research on the Teaching Reform of the “Decision Theory and Methods” Course Based on Integrating Human-Machine Collaboration with Evidence-Based Decision Making

摘要: 本研究聚焦于《决策理论与方法》课程教学中存在的“经验依赖、数据缺位、评价失衡”等决策困境，提出融合人机协同与循证决策理念的教学改革路径。基于“数据启发”范式，构建了教师认知系统与机器智能系统协同运行的循证教学决策模型，设计了覆盖教学计划、课堂互动与学习评价三阶段的“三阶循证”教学模式，实现从经验导向向证据支撑的教学决策转型。教学实践表明，该模式能够有效提升学生的决策认知能力、科研创新能力与反思能力，强化了课程的实践导向与方法论价值。研究实现了决策科学与教学实践、人工智能与教育智慧、数据证据与教育人文的三重融合，为研究生课程教学改革与高层次人才培养提供了新思路与实践范式。

Abstract: This study addresses critical challenges in the teaching of the “Decision Theory and Methods” course, including excessive reliance on teachers’ experience, lack of data-informed instructional decisions, and evaluation systems biased toward knowledge transmission. Drawing on the paradigm of data-informed decision-making, the research integrates human-machine collaboration and evidence-based practices to develop a dual-system instructional decision model combining machine intelligence with teacher cognition. A three-phase evidence-based instructional model—covering planning, in-class interaction, and evaluation—is designed to support a transition from experience-driven to evidence-supported teaching decisions. Empirical implementation demonstrates that the model enhances students’ decision-making cognition, research innovation, and reflective thinking, while reinforcing the course’s methodological and practical orientation. The study achieves a threefold integration of decision science and instructional practice, artificial intelligence and educator insight, and data evidence and educational values. It offers a novel approach for graduate-level curriculum reform and high-level talent cultivation in the context of intelligent education.