摘要
为了加强人工智能专业人才培养与地方产业发展需求相融合的能力,本文构建了一个动态和量化的人才培养考核模型。该模型以成果导向教育理念为基础,选取地方产业适配度、经济贡献与发展潜力、企业生产力与满意度、人才需求满足度及高校培养过程与能力等为核心影响因子,并通过层次分析法科学设定权重。同时,设计了可测量的二级指标体系,采用数据标准化与加权求和,计算综合得分,进而划定质量等级。最后,建立了“考核-反馈-改进”闭环机制,将考核结果逆向传送至人才培养环节,促进课程体系的持续优化与教学改革。该模型为评价与提升人工智能人才培养的能力提供了借鉴。
To enhance the capability of integrating the cultivation of artificial intelligence professionals with the needs of local industrial development,this paper constructs a dynamic and quantifiable assessment model for talent cultivation.Based on the concept of Outcome-Based Education(OBE),the model selects core influencing factors such as the alignment with local industries,economic contribution and development potential,enterprise productivity and satisfaction,the degree to which talent demand is met,and the higher education cultivation process and capabilities.The Analytic Hierarchy Process(AHP)is used to scientifically determine the weights of these factors.Meanwhile,a measurable set of secondary indicators is designed.Based on data standardization and weighted summation,a comprehensive score can be calculated,so as to define the quality levels.Finally,a closed-loop mechanism of"assessment-feedback-improvement"is established,which feeds the assessment results back to the talent cultivation process,thereby promoting the continuous optimization of the curriculum system and teaching reforms.This model provides a reference for evaluating and enhancing the quality of artificial intelligence talent cultivation.
作者
王双友
Shuangyou Wang(Software School,Handan University,Handan,056006,China)
基金
河北省高等教育教学改革研究与实践项目《与地方产业发展相融合的人工智能专业人才培养模式研究》支持资助(No.2023GJJG525)。
关键词
人工智能
人才培养
考核模型
持续改进
Artificial Intelligence
Talent Cultivation
Assessment Model
Continuous Improvement
