POLAR自2014年引入中国,并结合中国学生的特点进行了近10年的本地化研发与应用,形成了一套完善的专业/职业潜能评估体系。POLAR帮助数以万计的中国学生发现和发展个人潜力,为专业探索和职业生涯做好充分准备。POLAR已成为许多教育和职业指导机构的重要工具。

POLAR评估三个关键因素,包括:G因素(底层认知能力(General Aptitudes))、P因素(个人发展特质(Personal Attributes))和S因素(兴趣特长(Special Interests))。通过科学分析学生的潜能与兴趣,帮助其明确未来的优势专业领域。基于科学合理的规划,学生能够充分挖掘自身潜能和优势,找到通往Dream School的最佳路径。
GPS 模型
GPS 评估以借助 IRT 算法、机器学习、大数据分析等先进技术手段,进行数据驱动的精准迭代升级。 评估信效度已经过数据验证,指标已达到理想的心理测量学标准。项目分析显示,题总相关 0.73~0.94,各维度内高低分组均有显著差异(p<0.01);信度分析显示,内部一致系数 0.71~0.93;效度分析显示,χ2/df=4.653、CFI=0.940、TLI=0.927、RMSEA=0.059、SRMR=0.048。
G
底层认知能力
(General Aptitudes)
挖掘学生的认知潜能
预测未来的学业表现
5大底层认知能力:图形推理、空间知觉、数字推理、文字推理、言语理解
个人发展特质
(Personal Attributes)
评估学生的思维和行事特点
定位未来的优势领域
25项个人发展特质维度,包含:
领导力、社会责任性、心理弹性、
全局思维、社交能力、创造力、
决策能力等
P
S
兴趣特长
(Special Interests)
衡量学生个人兴趣与专业的关联度
预测未来的发展动力
54类专业方向,涵盖:数学、物理学、
哲学、心理学、金融、计算机科学、医学、法学等
自适应评估技术
GPS 评估借助国际前沿的自适应评估技术,让“量身定制”“千人千面”的测评成为现实,为学生提供了个性化、高效、精准的评估体验和人才发展最优解决方案。
个性化:GPS评估利用自适应技术,根据学生的实际能力,动态调整评估内容和难度,从而为每个学生提供个性化的评估体验。
高效:自适应技术能够根据学生的表现和反馈实时调整评估流程,优化评估过程,评估时间更短、结果更精准。
精准:通过持续优化评估题库、收集和分析评估数据,自适应技术可以更精准地评估学生的底层素养水平,从而为每个学生提供针对性的人才发展建议。
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