基于能力递进逻辑的统计类专业本研课程衔接机制研究——以“时间序列分析”为例

Research on the Articulation Mechanism of Undergraduate-Graduate Courses in Statistics Majors Based on the Logic of Progressive Competence—Taking “Time Series Analysis” as an Example

摘要: 目的：针对人工智能背景下统计类专业本科与研究生阶段存在的培养目标断层、课程内容脱节及创新能力递进不足等问题，本文探索以能力递进为核心的本研课程有效衔接机制，提升学生从方法掌握到综合分析与创新探索的培养质量。方法：以“时间序列分析”课程为对象，系统诊断本研两阶段在培养目标、教学内容、教学模式与评价体系四方面存在的衔接困境，构建以“能力阶梯”为主线的贯通式改革路径。结果：改革推动了教学内容的递进化，促进了学生从“知识使用者”向“问题提出者”的角色转变，初步形成了本研贯通的课程衔接体系。结论：基于能力递进逻辑构建的本研课程衔接机制，能够缓解统计类专业人才培养中的结构性断层问题，为强化创新人才连续性培养提供可操作、可推广的实践路径。

Abstract: Objective: In the context of artificial intelligence, in response to the problems of disconnected training objectives, fragmented course content, and insufficient progressive competence between undergraduate and graduate stages in statistics majors, this paper explores an effective articulation mechanism for undergraduate graduate courses centered on progressive competence, aiming to improve the quality of student training from method mastery to comprehensive analysis and innovative exploration. Methods: Taking the “Time Series Analysis” course as the research subject, this paper systematically diagnoses the articulation dilemmas between undergraduate and graduate stages in four aspects—training objectives, teaching content, teaching models, and evaluation systems—and constructs an integrated reform pathway with a “competence ladder” as the main thread. Results: The reform has promoted the progression of teaching content, facilitated students’ role transition from “knowledge users” to “problem proposers”, and preliminarily established an articulation system for undergraduate graduate courses. Conclusions: The articulation mechanism for undergraduate graduate courses constructed based on the logic of progressive competence can alleviate structural fragmentation issues in the cultivation of talents in statistics majors, providing an operable and scalable practical pathway for strengthening the continuous training of innovative talents.