Use of the K‐ABC with Children in Zaire,Africa: An Evaluation of the Sequential‐Simultaneous Processing Distinction within an Intercultural Context

There is a continuing need for formalised assessment procedures that can be adapted for group comparisons across diverse cultures to facilitate school and occupational placement, as well as to evaluate the effectiveness of nutritional, education, and medical intervention programs. This study used a direct translation of the Kaufman Assessment Battery for Children (K‐ABC) to evaluate 130 Zairian primary school children. The distinction between Sequential Processing and Simultaneous Processing abilities on the K‐ABC was generally found to be valid in this sample. The K‐ABC also discriminated effectively among grade levels, differences in health and family environment indicators, and tribal membership. Reasonable test‐retest correlations for the K‐ABC global scales were also found with a subsample of the children. The Zairian children's K‐ABC scores, particularly for the Simultaneous subtests, were markedly lower than the American normative sample. Education and experiential factors inherent in the sub‐Sahara environment may put these children at a significant cultural disadvantage in terms of the skills required for successful completion of K‐ABC items.     

Beyond vector space model for hierarchical Arabic text classification: A Markov chain approach

The vector space model (VSM) is a textual representation method that is widely used in documents classification. However, it remains to be a space-challenging problem. One attempt to alleviate the space problem is by using dimensionality reduction techniques, however, such techniques have deficiencies such as losing some important information. In this paper, we propose a novel text classification method that neither uses VSM nor dimensionality reduction techniques. The proposed method is a space efficient method that utilizes the first order Markov model for hierarchical Arabic text classification. For each category and sub-category, a Markov chain model is prepared based on the neighboring characters sequences. The prepared models are then used for scoring documents for classification purposes. For evaluation, we used a hierarchical Arabic text data collection that contains 11,191 documents that belong to eight topics distributed into 3-levels. The experimental results show that the Markov chains based method significantly outperforms the baseline system that employs the latent semantic indexing (LSI) method. That is, the proposed method enhances the F1-measure by 3.47%. The novelty of this work lies on the idea of decomposing words into sequences of characters, which found to be a promising approach in terms of space and accuracy. Based on our best knowledge, this is the first attempt to conduct research for hierarchical Arabic text classification with such relatively large data collection.     

Culturally Sustaining Practices in Content Area Instruction for CLD Students with Learning Disabilities

Culturally and linguistically diverse (CLD) students comprise a substantial population of students with learning disabilities, and are historically disproportionately represented in special education. To effectively teach CLD students with learning disabilities, teachers should integrate evidence‐based practices and culturally sustaining pedagogy in their practice. This article highlights several practical examples of culturally sustaining evidence‐based practices across the content areas of mathematics, reading, and writing. Suggestions for practice and future research are presented in the article.