A Story of English Language Learning – How Can Children’s Literature be Used in Teaching Vocabulary to Young English Language Learners? : - A Literature Review

This study aims to find research relating to the use of children’s literature to promote vocabulary development in young children, particularly English language learners in Sweden. The main questions address how (methods) children’s literature can be used and why (reasons) children’s literature is often recommended for the teaching of vocabulary to young learners. The study also aims to explore reasons against the use of children’s literature in vocabulary teaching found in previous research. A systematic literature review was carried out, including results from five empirical studies. The studies involved native speakers, second language learners and foreign language learners from various backgrounds. The results suggest that while research has shown children’s literature to be a good tool to use with young learners, careful lesson planning needs to be carried out. Direct instruction and scaffolding using pictures, technology and gestures is recommended. Hence, the teacher plays an important part for the vocabulary development using children’s literature in the classroom.

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.