智力测试比较：Slosson测试修订版与WISC-R在特殊教育和天才儿童中的应用

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"这篇论文是心理学在学校的期刊文章，发表于1986年7月，作者是Jerome M. Satter和Theron M. Covin，来自圣地亚哥州立大学的咨询与人类发展中心。研究对比了修订版的Slosson智力测试（SIT）和Wechsler智力量表修订版（WISC-R），在这两个测试中分别对有学习障碍的儿童和天才儿童进行了评估。" 在这项研究中，Slosson智力测试（SIT）的修订版和WISC-R被用于两组不同的儿童样本进行比较。第一组样本包括34名黑人和27名白人农村地区的阿拉巴马儿童，这些儿童可能需要特殊教育。第二组样本则有4名黑人和81名白人郊区的阿拉巴马儿童，他们正在考虑进入天才班。在两个样本中，SIT智商与WISC-R全量表智商之间的相关性均具有统计学意义（相关系数分别为0.70和0.48）。 然而，在特殊教育样本中，SIT智商显著高于WISC-R全量表智商约7分。这表明，尽管修订后的SIT标准在一定程度上得到了WISC-R作为标准的同时效度支持，但两个测试的智商得分可能并不完全可互换。这意味着它们可能对不同类型的儿童群体表现出不同的敏感性，特别是对于有特殊学习需求和天赋儿童的评估。 Slosson智力测试（SIT）的修订版更新了其标准化数据，以更好地适应不同群体。而WISC-R作为另一种广泛接受的智力评估工具，尤其在识别和理解学习障碍方面有着重要的应用。通过这两个测试的对比，研究者试图揭示哪种工具更能准确地反映特定儿童群体的智力水平，并提供有关如何在特殊教育和天才教育领域中选择适当的评估工具的见解。 这项研究为教育心理学家、心理咨询师以及特殊教育工作者提供了关于如何选择和解读不同智力测试结果的重要信息，强调了在评估儿童智力时，不仅要考虑测试的效度，还要考虑其可能存在的文化、社会和个体差异的影响。同时，这项工作也为未来在智力评估领域内的研究提供了基础，以进一步改进和优化测试方法，确保公平且准确地评估所有儿童的能力。

A comparison of the Slosson intelligence test and the WISC-R with elementary school children

2021-06-29 上传
A comparison of the Slosson intelligence test and the WISC-R with elementary school children A COMPARISON OF THE SLOSSON INTELLIGENCE SCHOOL CHILDREN TEST AND THE WISC-R WITH ELEMENTARY U.4N ...

slosson 智力测验、修订规范和 WISC-R 对有学习问题儿童和天才儿童的比较

2021-06-29 上传
Comparison of the slosson intelligence test, revised norms, and WISC-R for children with learning problems and for gifted children Psychology in the Schools Volume 23, July 1986 COMPARISON OF THE...

Comparison of the slosson intelligence test and wisc scores of subjects referred to a reading clinic

2021-06-29 上传
Comparison of the slosson intelligence test and wisc scores of subjects referred to a reading clinic COMPARISON OF THE SLOSSON INTELLIGENCE TEST AND WISC SCORES O F SUBJECTS REFERRED TO A READING ...

4 Experiments This section examines the effectiveness of the proposed IFCS-MOEA framework. First, Section 4.1 presents the experimental settings. Second, Section 4.2 examines the effect of IFCS on MOEA/D-DE. Then, Section 4.3 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on 19 test problems. Finally, Section 4.4 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on four real-world application problems. 4.1 Experimental Settings MOEA/D-DE [23] is integrated with the proposed framework for experiments, and the resulting algorithm is named IFCS-MOEA/D-DE. Five surrogate-based MOEAs, i.e., FCS-MOEA/D-DE [39], CPS-MOEA [41], CSEA [29], MOEA/DEGO [43] and EDN-ARM-OEA [12] are used for comparison. UF1–10, LZ1–9 test problems [44, 23] with complicated PSs are used for experiments. Among them, UF1–7, LZ1–5, and LZ7–9 have 2 objectives, UF8–10, and LZ6 have 3 objectives. UF1–10, LZ1–5, and LZ9 are with 30 decision variables, and LZ6–8 are with 10 decision variables. The population size N is set to 45 for all compared algorithms. The maximum number of FEs is set as 500 since the problems are viewed as expensive MOPs [39]. For each test problem, each algorithm is executed 21 times independently. For IFCS-MOEA/D-DE, wmax is set to 30 and η is set to 5. For the other algorithms, we use the settings suggested in their papers. The IGD [6] metric is used to evaluate the performance of each algorithm. All algorithms are examined on PlatEMO [34] platform.

2023-05-24 上传
4 实验 本节将研究所提出的IFCS-MOEA框架的有效性。首先，在第4.1节中介绍实验设置。其次，在第4.2节中研究IFCS对MOEA/D-DE的影响。然后，在第4.3节中，将IFCS-MOEA/D-DE与19个测试问题上的五种最先进的MOEA进行比较...

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2023-04-07 上传
- Discuss the different methods used for lane detection such as Hough Transform, Canny Edge Detection, and Deep Learning Slide 3: Hough Transform - Explain the Hough Transform method for lane ...

Traditional network security situation prediction methods depend on the accuracy of historical situation value. Moreover, there are differences in correlation and importance among various network security factors. In order to solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO) was proposed. This model was first decomposed and reconstructed into a series of subsequences through the SSA of network security situation data. Next, a prediction model of TCAN-BiGRU was established for each subsequence, respectively. The TCN with relatively simple structure was used in the TCAN to extract features from the data. Besides, the improved channel attention mechanism (CAM) was used to extract important feature information from TCN. Afterwards, the before-after status of the learning situation value of the BiGRU neural network was used to extract more feature information from sequences for prediction. Meanwhile, an improved IQPSO was proposed to optimize the hyper-parameter of the BiGRU neural network. Finally, the prediction results of subsequence were superimposed to obtain the final predicted value. In the experiment, on the one hand, the IQPSO was compared with other optimization algorithms; and the results showed that the IQPSO has better optimization performance; on the other hand, the comparison with traditional prediction methods was performed through the simulation experiment and the established prediction model; and the results showed that the combined prediction model established has higher prediction accuracy.

2023-02-19 上传
答：基于时间卷积注意网络（TCAN）和双向门驱动单元（BiGRU）网络，结合奇异谱分析（SSA）和改进量子粒子群优化算法（IQPSO）的联合预测模型，解决了传统网络安全态势预测方法依赖于历史态势值的准确性，以及各网络...

This approach is justified given the comparable auto-HCT outcomes before and after 2018 (data not shown). Comparison between CAR-T and auto-HCT was made using propensity score matching as well. Although the effect on disease relapse/progression and mortality was directionality consistent with the primary analysis,itwasnotstatisticallysignificant. This subset analysis reduced the sample size, which likely affected the power to detect statistical significance. The observed difference was mainly observed in patients with $2 prior lines of treatment. This cutoff was used for subgroup analysis, given the implications in clinical practice. Although lack of statistical significance in patients with ,2 prior lines of treatment warrants further attention and ideally prospective trials in that group, the small number of patients in this subgroup analysis limits our ability for making a strong conclusion, particularly because we did not observeaninteraction(effectmodification) between the number of prior treatment lines and treatment modality and outcomes. Patients in the CAR-T group were more heavily pretreated and it is possible that the CAR-T may show improved efficacy in patients with fewer lines of prior treatment Last, we only included patients treated with axi-cel to reduce the heterogeneity given the small number of patients treated with tisa-cel in the registry, fulfilling eligibility criteria for this study at the time of the analysis.翻译

2023-07-23 上传
鉴于2018年前后自体造血干细胞移植(auto-HCT)的结果可比(未显示数据)，这种方法是合理的。CAR-T和auto-HCT之间的比较也是使用倾向性评分匹配进行的。尽管对于疾病复发/进展和死亡的影响与主要分析的方向一致，但在...

The modified CNN architecture was able to provide joint information of multiple modalities for the DLR analysis. The performance of multiple modalities was evaluated using the dataset of the first cohort with both T2 flair images and T1 contrast images. Two evaluation methods were used: leave-one-out cross-validation and validation based on time of diagnosis. The evaluation parameters of the prediction results based on leave-one-out cross-validation are shown in Table 2(b), and a comparison of the ROC curves is presented in Fig. 3(b). 解释

2023-06-10 上传
这段话主要说明了一个修改过的卷积神经网络(CNN)架构可以提供多个模态的联合信息进行 DLR(深度学习诊断)分析，并使用第一组数据集中的T2 FLAIR图像和T1对比图像来评估多个模态的性能。作者使用了两种评估方法：留一...

comparison of unsigned expression

2023-05-13 上传
Overall, when performing comparisons of unsigned expressions, it is important to be aware of the behavior of the comparison operators and to ensure that the values being compared are of the same size ...

根据这个框架写一篇关于《自主学习中基于多模态数据的学习风格高精度识别》的文章框架如下：一、介绍。 A.研究背景 B.研究目的 C.研究意义 二、文献综述 A.学习风格概述 B.以往关于学习风格识别的研究 C.多模态数据概述 D.多模态数据集成和学习风格识别 三、方法 A.数据收集 B.数据预处理 C.特征提取 D.模型选择 E.模型评价 四、结果 A.结果概述 B.与以往研究的准确性比较 C.分析不同模式对识别精度的影响 D.讨论结果 五、结论 A.调查结果摘要 B.本研究的局限性 C.研究的意义 D.对未来研究的建议 六。参考文献

2023-02-14 上传
The study seeks to identify the most effective combination of modalities for recognizing learning styles and to compare the results with previous research. C. Research Significance The results of ...
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