城市学生特质与团体咨询方法的互动关系分析

需积分: 5 0 下载量 90 浏览量 更新于2024-08-07 收藏 449KB PDF 举报
"这篇论文探讨了学生特性与团体咨询方法之间的互动关系,主要关注在城市儿童样本中的应用。文章指出,两个MRT(可能是心理测量工具)子测验的可靠性较低,涉及对有意义的听觉符号,即言语的理解。尽管其他子测验的可靠性看似足够(大于0.70),但这些可靠性应当与项目分析相结合来判断该测量工具对于这个特定样本的适用性。发现MRT和MAT(可能是另一种心理测量工具)的项目难度过高,只有35%的MRT项目和21%的MAT项目同时具有可接受的难度和效度水平。此外,MRT项目的中位难度为0.30,MAT项目的中位难度为0.19。Goodstein, Whitney, 和 Cawley (1970)的研究也得出了类似的结论,即对于二年级弱势群体儿童,MRT虽然在统计上能有效预测阅读成就,但在区分个体成功和失败读者方面却不具备诊断性的实用性。他们还指出,MRT包含过多的难题。因此,根据这些数据可以推断,在尝试衡量学生的成就时,必须考虑到测试工具的难度和适应性,特别是对于特殊群体,如城市弱势儿童,可能需要更为适宜的评估手段。" 这篇学术论文深入研究了在城市儿童群体中,学生个体特征如何与团体心理咨询方法相互作用。它指出,用于评估学生能力的心理测量工具,如MRT和MAT,其可靠性和有效性可能因样本的特异性而受到影响。MRT在理解言语等复杂任务上的低可靠性提示,可能需要对这些工具进行重新评估,以确保它们能准确反映这个特定群体的能力。同时,这些工具的项目难度过高,导致大部分项目对于这个样本来说过于困难,这可能会影响测试结果的解释和诊断价值。研究者引用了其他研究,进一步证明了这一观点,即MRT虽然在统计上与阅读成就相关,但在区分不同阅读水平的学生方面并不理想。 这篇论文强调了在设计和应用心理测量工具时,需要考虑目标群体的特性,尤其是对于教育环境中的弱势群体。它提倡更细致的项目分析,以确定测试的适宜性,并且可能需要开发更适合特定学生群体的评估方法,以便更有效地识别他们的学习需求和能力。这对于提升团体咨询的有效性和个性化教学策略的制定具有重要意义。

Please revise the paper:Accurate determination of bathymetric data in the shallow water zone over time and space is of increasing significance for navigation safety, monitoring of sea-level uplift, coastal areas management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustics measurements over coastal areas with high spatial and temporal resolution combined with extensive repetitive coverage. Numerous empirical SDB approaches in previous works are unsuitable for precision bathymetry mapping in various scenarios, owing to the assumption of homogeneous bottom over the whole region, as well as the limitations of constructing global mapping relationships between water depth and blue-green reflectance takes no account of various confounding factors of radiance attenuation such as turbidity. To address the assumption failure of uniform bottom conditions and imperfect consideration of influence factors on the performance of the SDB model, this work proposes a bottom-type adaptive-based SDB approach (BA-SDB) to obtain accurate depth estimation over different sediments. The bottom type can be adaptively segmented by clustering based on bottom reflectance. For each sediment category, a PSO-LightGBM algorithm for depth derivation considering multiple influencing factors is driven to adaptively select the optimal influence factors and model parameters simultaneously. Water turbidity features beyond the traditional impact factors are incorporated in these regression models. Compared with log-ratio, multi-band and classical machine learning methods, the new approach produced the most accurate results with RMSE value is 0.85 m, in terms of different sediments and water depths combined with in-situ observations of airborne laser bathymetry and multi-beam echo sounder.

2023-02-18 上传