Ability-grouping and problem behavior trajectories in childhood and adolescence: Results from a U.K. population-based sample

Ability-grouping has been studied extensively in relation to children's academic, but not emotional and behavioral outcomes. The sample comprised 7259 U.K. children (50% male) with data on between-class and within-class ability-grouping at age 7. Peer, emotional, hyperactivity, and conduct problems were measured at ages 7, 11, and 14 years. Children in low within-class ability groups showed more hyperactivity and emotional problems across the study period compared to non-grouped children, after adjustments for the different types of ability grouping and confounding. Additionally, children in the middle within-class ability groups showed more, and those in the top within-class groups less, hyperactivity compared to non-grouped children, after adjustment. Children in lower within-class groups should be monitored closely to ensure that their well-being is not compromised.     

Learning to recognize webpage genres

Webpages are mainly distinguished by their topic (e.g., politics, sports etc.) and genre (e.g., blogs, homepages, e-shops, etc.). Automatic detection of webpage genre could considerably enhance the ability of modern search engines to focus on the requirements of the user’s information need. In this paper, we present an approach to webpage genre detection based on a fully-automated extraction of the feature set that represents the style of webpages. The features we propose (character n-grams of variable length and HTML tags) are language-independent and easily-extracted while they can be adapted to the properties of the still evolving web genres and the noisy environment of the web. Experiments based on two publicly-available corpora show that the performance of the proposed approach is superior in comparison to previously reported results. It is also shown that character n-grams are better features than words when the dimensionality increases while the binary representation is more effective than the term-frequency representation for both feature types. Moreover, we perform a series of cross-check experiments (e.g., training using a genre palette and testing using a different genre palette as well as using the features extracted from one corpus to discriminate the genres of the other corpus) to illustrate the robustness of our approach and its ability to capture the general stylistic properties of genre categories even when the feature set is not optimized for the given corpus.     

Distance Learning for Teacher Professional Development in Statistics Education

We provide an overview of EarlyStatistics, an online professional development course in statistics education targeting European elementary and middle school teachers. The course facilitates intercultural collaboration of teachers using contemporary technological and educational tools. An online information base offers access to all of the course content and resources.     

Author identification: Using text sampling to handle the class imbalance problem

Authorship analysis of electronic texts assists digital forensics and anti-terror investigation. Author identification can be seen as a single-label multi-class text categorization problem. Very often, there are extremely few training texts at least for some of the candidate authors or there is a significant variation in the text-length among the available training texts of the candidate authors. Moreover, in this task usually there is no similarity between the distribution of training and test texts over the classes, that is, a basic assumption of inductive learning does not apply. In this paper, we present methods to handle imbalanced multi-class textual datasets. The main idea is to segment the training texts into text samples according to the size of the class, thus producing a fairer classification model. Hence, minority classes can be segmented into many short samples and majority classes into less and longer samples. We explore text sampling methods in order to construct a training set according to a desirable distribution over the classes. Essentially, by text sampling we provide new synthetic data that artificially increase the training size of a class. Based on two text corpora of two languages, namely, newswire stories in English and newspaper reportage in Arabic, we present a series of authorship identification experiments on various multi-class imbalanced cases that reveal the properties of the presented methods.