Social Perceptions of Students with Learning Disabilities Who Differ in Social Status

As a group, students with learning disabilities (LD) have social difficulties. One possible explanation for these difficulties is the unique way they process social information. Although students with LD may differ from their nondisabled peers in their social cognition, investigators have suggested the presence of subgroups within the population of students with LD who may differ in their social competence and, thereby, shed light on the source of the difficulties. The present exploratory study examined how two subgroups of students with LD in inclusive settings, students with high and low social status, perceive social situations. Using a sociometric technique, three students with LD receiving high social‐status ratings and three students with LD receiving low social‐status ratings were identified. A qualitative approach was used to gather and evaluate data from the participants and their teachers. Results suggested differences between the two subgroups in their (1) sensitivity to cues in the environment, (2) interpretation of social situations in relation to their own experiences, and (3) levels of self‐control. Implications of these findings for practice and future research are discussed.     

Adapting Adaptive Testing: Using the MicroCAT Testing System in a Local School District

What is the rationale for adapting an existing testing system instead of developing your own? What are the limitations of MicroCAT? What has to be modified in order to meet local needs and to realize the potential of adaptive testing in the context of an existing testing system?     

浅谈如何上好艺术体操选修课

对普通高校艺术体操选修课教学从形体姿态的培养、教学成套动作的编排,音乐的选择及口令、手势的运用等,进行了分析探讨.     

A Corpus-Based Learning Method of Compound Noun Indexing Rules for Korean

In Korean information retrieval, compound nouns play an important role in improving precision in search experiments. There are two major approaches to compound noun indexing in Korean: statistical and linguistic. Each method, however, has its own shortcomings, such as limitations when indexing diverse types of compound nouns, over-generation of compound nouns, and data sparseness in training. In this paper, we propose a corpus-based learning method, which can index diverse types of compound nouns using rules automatically extracted from a large corpus. The automatic learning method is more portable and requires less human effort, although it exhibits a performance level similar to the manual-linguistic approach. We also present a new filtering method to solve the problems of compound noun over-generation and data sparseness.