General self-concept and life satisfaction for boys with differing levels of physical coordination: The role of goal orientations and leisure participation

Participation in leisure-time activities, self-concept perceptions and individual dispositional goal orientations were examined as mediators of relationships between physical coordination and self-evaluations of life satisfaction and general self-concept for 173 boys aged 10-13 years. Participants completed seven-day activity diaries and 12-month retrospective recall questionnaires recording participation in leisure-time activities. Self-report measures of self-concept, global life satisfaction and dispositional goal orientations were also completed. Results showed that boys with moderate to severe physical coordination difficulties had significantly lower self-concept perceptions of physical ability and appearance, peer and parent relations and general self-concept, as well as lower life satisfaction than boys with medium to high levels of physical coordination. The relationships between boys' physical coordination and their self-perceptions of life satisfaction and general self-concept were significantly influenced by individual self-concept appraisals of physical ability and appearance, peer and parent relations. Adopting task-oriented goals was found to positively change the relationship between physical coordination and both general self-concept and life satisfaction. Team sport participation positively mediated the relationship between physical coordination and life satisfaction. The potential for team sport participation and adoption of task-oriented goals to influence life satisfaction for boys with differing levels of physical coordination was discussed. (c) 2006 Elsevier B.V.. All rights reserved.

A web recommendation technique based on probabilistic latent semantic analysis

Web transaction data between Web visitors and Web functionalities usually convey user task-oriented behavior pattern. Mining such type of click-stream data will lead to capture usage pattern information. Nowadays Web usage mining technique has become one of most widely used methods for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining, such as Web user session or Web page clustering, association rule and frequent navigational path mining can only discover usage pattern explicitly. They, however, cannot reveal the underlying navigational activities and identify the latent relationships that are associated with the patterns among Web users as well as Web pages. In this work, we propose a Web recommendation framework incorporating Web usage mining technique based on Probabilistic Latent Semantic Analysis (PLSA) model. The main advantages of this method are, not only to discover usage-based access pattern, but also to reveal the underlying latent factor as well. With the discovered user access pattern, we then present user more interested content via collaborative recommendation. To validate the effectiveness of proposed approach, we conduct experiments on real world datasets and make comparisons with some existing traditional techniques. The preliminary experimental results demonstrate the usability of the proposed approach.

Finding temporal features of event-oriented patterns

A major task of traditional temporal event sequence mining is to predict the occurrences of a special type of event (called target event) in a long temporal sequence. Our previous work has defined a new type of pattern, called event-oriented pattern, which can potentially predict the target event within a certain period of time. However, in the event-oriented pattern discovery, because the size of interval for prediction is pre-defined, the mining results could be inaccurate and carry misleading information. In this paper, we introduce a new concept, called temporal feature, to rectify this shortcoming. Generally, for any event-oriented pattern discovered under the pre-given size of interval, the temporal feature is the minimal size of interval that makes the pattern interesting. Thus, by further investigating the temporal features of discovered event-oriented patterns, we can refine the knowledge for the target event prediction.

Finding event-oriented patterns in long temporal sequences

A major task of traditional temporal event sequence mining is to find all frequent event patterns from a long temporal sequence. In many real applications, however, events are often grouped into different types, and not all types are of equal importance. In this paper, we consider the problem of efficient mining of temporal event sequences which lead to an instance of a specific type of event. Temporal constraints are used to ensure sensibility of the mining results. We will first generalise and formalise the problem of event-oriented temporal sequence data mining. After discussing some unique issues in this new problem, we give a set of criteria, which are adapted from traditional data mining techniques, to measure the quality of patterns to be discovered. Finally we present an algorithm to discover potentially interesting patterns.