The Role of Motivational Strategies in the Upper Elementary Classroom : A systematic literature review about teacher strategies that promote self-confidence, motivation and a conducive learning environment

The aim of this literature review is to investigate which strategies teachers use to motivate pupils to communicate orally in English. The literature review also investigates how these teacher strategies affect pupils. The methodology used for this investigation is a systematic literature review. Various databases have been used when searching for literature. Scientific articles and theses have been searched for. They have also been read and analyzed before they have become a part of this review. The results indicate that some teachers feel insecure when speaking English. Therefore Swedish is spoken in many language classrooms. Teachers speaking in front of the class is the traditional way of teaching, and it does not seem to be a strategy who influences pupils positively. If teachers speak the target language among pupils they often get more motivated and focused pupils who feel comfortable speaking English. Young pupils are fast learners. By exposing them to the English language in early ages they receive great opportunities to learn a foreign language and strengthen their self-confidence. Drama, songs and rhymes are preferable strategies to use when teaching young learners. What position teachers decide to take in the classroom is also a significant element when teaching foreign languages.

Assumptions of retail price strategy and price tactic decisions

This licentiate thesis sets out to analyse how a retail price decision frame can be understood. It is argued that it is possible to view price determination within retailing by determining the level of rationality and using behavioural theories. In this way, it is possible to use assumptions derived from economics and marketing to establish a decision frame. By taking a management perspective, it is possible to take into consideration how it is assumed that the retailer should strategically manage price decisions, which decisions might be assumed to be price decisions, and which decisions can be assumed to be under the control of the retailer. Theoretically, this licentiate thesis has its foundations in different assumptions about decision frames regarding the level of information collected, the goal of the decisions, and the outcomes of the decisions. Since the concepts that are to be analysed within this thesis are price decisions, the latter part of the theory discusses price decision in specific: sequential price decisions, at the point of the decision, and trade-offs when making a decision. Here, it is evident that a conceptual decision frame that is intended to illustrate price decisions includes several aspects: several decision alternatives and what assumptions of rationality that can be made in relation to the decision frame. A semi-structured literature review was conducted. As a result, it became apparent that two important things in the decision frame were unclear: time assumptions regarding the decisions and the amount of information that is assumed in relation to the different decision alternatives. By using the same articles that were used to adjust the decision frame, a topical study was made in order to determine the time specific assumptions, as well as the analytical level based on the assumed information necessary for individual decision alternatives. This, together with an experimental study, was necessary to be able to discuss the consequences of the rationality assumption. When the retail literature is analysed for the level of rationality and consequences of assuming certain assumptions of rationality, three main things becomes apparent. First, the level of rationality or the assumptions of rationality are seldom made or accounted for in the literature. In fact, there are indications that perfect and bounded rationality assumptions are used simultaneously within studies. Second, although bounded rationality is a recognised theoretical perspective, very few articles seem to use these assumptions. Third, since the outcome of a price decision seems to provide no incremental sale, it is questionable which assumptions of rationality that should be used. It might even be the case that no assumptions of rationality at all should be used. In a broader perspective, the findings from this licentiate thesis show that the assumptions of rationality within retail research is unclear. There is an imbalance between the perspectives used, where the main assumptions seem to be concentrated to perfect rationality. However, it is suggested that by clarifying which assumptions of rationality that is used and using bounded rationality assumptions within research would result in a clearer picture of the multifaceted price decisions that could be assumed within retailing. The theoretical contribution of this thesis mainly surround the identification of how the level of rationality provides limiting assumptions within retail research. Furthermore, since indications show that learning might not occur within this specific context it is questioned whether the basic learning assumption within bounded rationality should be used in this context.

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.