数字化学习生态下超声医学教学机制重构研究

Research on the Restructuring of Ultrasound Medical Teaching Mechanisms under the Digital Learning Ecosystem

摘要: 目的：探讨数字化学习生态(指由学生、教师、人工智能、平台工具、学习资源及交互反馈共同参与构成的动态学习系统)下医学生超声学习路径、教师角色认知及评价体系与学习生态适配性的特征，分析其与学习结果之间的关系，并探索超声医学教学重构路径。方法：本研究为横断面调查研究。采用问卷调查和课程成绩分析方法，选取福建中医药大学修读超声医学相关课程的本科生126名，回收有效问卷118份，其中112名学生可完成后继问卷与成绩匹配分析。问卷围绕学习路径迁移、教师角色认知和评价体系适配性三个维度设计；同时收集理论成绩、上机操作考核成绩及病例分析成绩。采用描述性统计、组间比较及多元线性回归分析。结果：学生最常使用的学习资源为短视频、课程平台及AI辅助工具，62.7%的学生在遇到学习问题时优先检索数字资源或AI，仅12.7%的学生优先请教教师。学习路径方面，高学习路径迁移组理论成绩高于低迁移组(P < 0.001)，但上机操作考核成绩差异无统计学意义(P = 0.586)。教师角色认知方面，高认知组上机操作考核成绩和病例分析成绩均高于低组(P < 0.05)。在评价体系方面，评价体系适配不足得分较高者病例分析成绩低于较低者(P = 0.002)。多元线性回归分析显示，学习路径迁移与理论成绩独立相关(P = 0.001)；教师角色认知与上机操作考核成绩及病例分析成绩(P = 0.004)独立相关；评价体系与不足与病例分析成绩呈负相关(P = 0.006)。结论：数字化学习生态正在从知识输入、教学支持和评价导向三个层面重塑超声医学教学，推动其从“知识中心型”向“能力导向型”教学转型。

Abstract: Objective: To investigate the characteristics of ultrasound learning pathways, teacher role perception, and the adaptability between the assessment system and the learning ecosystem among medical students within a digital learning ecosystem (a dynamic learning system jointly shaped by students, teachers, artificial intelligence, platform-based tools, learning resources, and interactive feedback), to analyze their associations with learning outcomes, and to explore potential directions for restructuring ultrasound education. Methods: This was a cross-sectional survey. Questionnaire data and course performance data were collected from 126 undergraduate students taking ultrasound-related courses at Fujian University of Traditional Chinese Medicine. A total of 118 valid questionnaires were returned, of which 112 could be matched with course performance for subsequent analysis. The questionnaire covered three dimensions: learning pathway migration, perceptions of teacher roles, and the alignment between the assessment system and the learning ecosystem. Theoretical examination scores, hands-on operational assessment scores, and case-analysis scores were also collected. Descriptive statistics, between-group comparisons, and multiple linear regression analyses were performed. Results: The most commonly used learning resources were short videos, course platforms, and AI-assisted tools. A total of 62.7% of students reported that they first turned to digital resources or AI when encountering learning difficulties, whereas only 12.7% first consulted teachers. With respect to learning pathways, students in the high migration group had higher theoretical examination scores than those in the low migration group (P < 0.001), whereas no significant difference was found in hands-on operational assessment scores (P = 0.586). Regarding perceptions of teacher roles, students in the high-perception group scored higher on both hands-on operational assessment and case analysis than those in the low-perception group (both P < 0.05). Regarding the assessment system, students with higher evaluation system misalignment scores had lower case analysis scores than those with lower scores (P = 0.002). Multiple linear regression showed that learning pathway migration was independently associated with theoretical examination scores (P = 0.001); perceptions of teacher roles were independently associated with hands-on operational assessment scores (P = 0.002) and case-analysis scores (P = 0.004); and poorer alignment between the assessment system and the learning ecosystem was negatively associated with case-analysis scores (P = 0.006). Conclusion: The digital learning ecosystem is fundamentally reshaping ultrasound medical education by operating simultaneously on three levels: knowledge acquisition, pedagogical support, and evaluation frameworks. This multidimensional transformation is progressively shifting the educational model from a traditional knowledge-centric approach toward a competency-based paradigm.