Georgia College & State University

About: Georgia College & State University is a education organization based out in Milledgeville, Georgia, United States. It is known for research contribution in the topics: Population & Context (language use). The organization has 950 authors who have published 1591 publications receiving 37027 citations.

Indonesian students’ perceptions of choice criteria in the selection of a tertiary institution: strategic implications

Abstract: Intense competition for full‐fee‐paying foreign students in higher education in many countries mandates the need for the identification of the criteria considered important by this customer group for the purpose of strategy development. Past research in this area has overlooked the needs of this important segment and has focused mainly on either administrators or the faculty’s perspective. A sample of potential students from Indonesia participated in this study and a number of choice criteria were identified. Also discusses strategic implications.

Emotional Intelligence: A Theoretical Overview and Implications for Research and Professional Practice in Sport Psychology

Abstract: Over the past five years, sport psychology researchers and practitioners have become increasingly vocal in their suggestions that emotional intelligence (EI) may be an important construct in the sport domain. Initial research in sport has been valuable for gaining preliminary insights, but use of disparate theoretical frameworks and assessment techniques confuses rather than clarifies potential links between EI and sport. Specifically, the use of different definitions, conceptualizations, and assessment inventories may yield different EI profiles of the same individual or team. This disparity has important implications for applied sport psychology, where there is a call for the use of theoretical paradigms, objective and subjective assessments, and empirical research to inform practice. The purposes of this paper, therefore, are to: (a) review EI models and assessment inventories; (b) review research on EI in business, health, and sport; and (c) identify directions for future research and professional pra...

Efficient data mining for maximal frequent subtrees

Abstract: A new type of tree mining is defined, which uncovers maximal frequent induced subtrees from a database of unordered labeled trees. A novel algorithm, PathJoin, is proposed. The algorithm uses a compact data structure, FST-Forest, which compresses the trees and still keeps the original tree structure. PathJoin generates candidate subtrees by joining the frequent paths in FST-Forest. Such candidate subtree generation is localized and thus substantially reduces the number of candidate subtrees. Experiments with synthetic data sets show that the algorithm is effective and efficient.