Developmental changes in cognitive persistence and academic achievement between grade 4 and grade 8

This study describes changes in cognitive persistence, a key measure of mastery motivation, between the ages of 10 (grade 4) and 14 (grade 8). Prior research in the field of mastery motivation has focused mainly on early childhood. No longitudinal research findings have been published about age changes in mastery motivation during the school years. The sample of this longitudinal study consisted of 372 students in Hungary and was representative of the Hungarian school population regarding parents’ level of education. Participants were in 25 school classes, each from a different town. The instrument was the cognitive persistence scale of the Hungarian version of the Dimensions of Mastery Questionnaire (DMQ). Grade point average (GPA), parents’ level of education, the size of the class and of the town were analyzed as background variables. Cognitive persistence based on students’ self-reports decreased substantially in 61 % of the students, did not change in 33 %, and increased in only 6 % over the four school years. Changes in cognitive persistence correlated with changes in GPA. Significant differences were found among school classes in both the average level of cognitive persistence and in how much it changed. However, neither cognitive persistence nor changes in cognitive persistence correlated with parents’ level of education or town size. Aspects of the school and classroom climate seem to substantially impact changes in students’ cognitive persistence. In addition, this study considered educational applications and for further research.     

A language modeling framework for expert finding

Statistical language models have been successfully applied to many information retrieval tasks, including expert finding: the process of identifying experts given a particular topic. In this paper, we introduce and detail language modeling approaches that integrate the representation, association and search of experts using various textual data sources into a generative probabilistic framework. This provides a simple, intuitive, and extensible theoretical framework to underpin research into expertise search. To demonstrate the flexibility of the framework, two search strategies to find experts are modeled that incorporate different types of evidence extracted from the data, before being extended to also incorporate co-occurrence information. The models proposed are evaluated in the context of enterprise search systems within an intranet environment, where it is reasonable to assume that the list of experts is known, and that data to be mined is publicly accessible. Our experiments show that excellent performance can be achieved by using these models in such environments, and that this theoretical and empirical work paves the way for future principled extensions.     

Blog feed search with a post index

User generated content forms an important domain for mining knowledge. In this paper, we address the task of blog feed search: to find blogs that are principally devoted to a given topic, as opposed to blogs that merely happen to mention the topic in passing. The large number of blogs makes the blogosphere a challenging domain, both in terms of effectiveness and of storage and retrieval efficiency. We examine the effectiveness of an approach to blog feed search that is based on individual posts as indexing units (instead of full blogs). Working in the setting of a probabilistic language modeling approach to information retrieval, we model the blog feed search task by aggregating over a blogger’s posts to collect evidence of relevance to the topic and persistence of interest in the topic. This approach achieves state-of-the-art performance in terms of effectiveness. We then introduce a two-stage model where a pre-selection of candidate blogs is followed by a ranking step. The model integrates aggressive pruning techniques as well as very lean representations of the contents of blog posts, resulting in substantial gains in efficiency while maintaining effectiveness at a very competitive level.