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Informatics or computer science (the terms informatics and computer science are used as synonyms in this paper) in Austrian secondary schools is often considered “difficult” and “not interesting” because of mainly mathematical and economical contents. But informatics is much more; it is used in almost every part of the working life and can also cooperate with every subject at school. It offers a wide range of possibilities for COOL (COoperative Open Learning), an Austrian teaching model based on the Dalton Plan that provides also cooperation between different subject matters. Besides the COOL concept this paper describes some ideas for cooperation between informatics and foreign languages at secondary schools and at University level besides just ICT (Information and Communication Technology) or computer-assisted language learning. The presented tasks are part of the project “COOL Informatics” (according to COoperative Open Learning) that aims at developing COOL teaching material based on neurodidactical (Neurodidactics is a relatively young research field that combines the findings of brain research, pedagogy, cognitive psychology and other related fields.) principles for informatics and cross-curricular cooperation from primary school up to University. On the basis of the developed material the following main research questions of the project shall be examined in the course of the next school year: (1) Can COoperative Open Learning methods help to enhance learning? (2) Can learning be enhanced by considering neurodidactical principles in the design of teaching material and in the classroom? As the first test phase of the developed material is still in progress the evaluation is not available at the moment. But the first informal feedback of a vocational school and a programming course at the University shows that it is worth fostering COoperative Open Learning and cross-curricular cooperation. [Copyright &y& Elsevier]

This paper explores the utility of machine learning methods for understanding bullying, a significant social-psychological issue in the United States, through social media data. Machine learning methods were applied to all public mentions of bullying on Twitter between September 1, 2011 and August 31, 2013 to extract the posts that referred to discrete bullying episodes ( N = 9,764,583) to address five key questions. Most posts were authored by victims and reporters and referred to general forms of bullying. Posts frequently reflected self-disclosure about personal involvement in bullying. The number of posts that originated from a state was positively associated with the state population size; the timing of the posts reveal that more posts were made on weekdays than on Saturdays and more posts were made during the evening compared to daytime hours. Potential benefits of merging social science and computer science methods to enhance the study of bullying are discussed. [ABSTRACT FROM AUTHOR]