The objective of this thesis is to investigate the possibility of using machine learn- ing at Statistics Sweden within the Foreign Trade in Services (FTS) statistic, to predict the likelihood of a unit to conduct foreign trade in services. The FTS survey is a sample survey, for which there is no natural frame to sample from. Therefore, prior to sampling a frame is manually constructed each year, starting with a register of all Swedish companies and agencies and in a rule- based manner narrowing it down to contain only what is classified as units likely to trade in services during the year to come. An automatic procedure that would enable reliable predictions is requested. To this end, three different machine learning methods have been analyzed, two rule- based methods (random forest and extreme gradient boosting) and one distance- based method (k nearest neighbors). The models arising from these methods are trained and tested on historically sampled units, for which it is known whether they did trade or not. The results indicate that the two rule-based methods perform well in classifying likely traders. The random forest model is better at finding traders, while the extreme gradient boosting model is better at finding non-traders. The results also indicate interesting patterns when studying different metrics for the models. The results also indicate that when training the rule-based models, the year in which the training data was sampled needs to be taken into account. This entails that cross-validation with random folds should not be used, but rather grouped cross-validation based on year. By including a feature that mirror the state of the economy, the model can adapt its rules to this, meaning that the rules learned on training data can be extended to years beyond training data. Based on the observed results, the final recommendation is to further develop and investigate the performance of the random forest model.

Legislative committee meetings are an integral part of the lawmaking process for local and state bills. The testimony presented during these meetings is a large factor in the outcome of the proposed bill. This research uses Natural Language Processing and Machine Learning techniques to analyze testimonies from California Legislative committee meetings from 2015-2016 in order to identify what aspects of a testimony makes it successful. A testimony is considered successful if the alignment of the testimony matches the bill outcome (alignment is "For" and the bill passes or alignment is "Against" and the bill fails). The process of finding what makes a testimony successful was accomplished through data filtration, feature extraction, implementation of classification models, and feature analysis. Several features were extracted and tested to find those that had the greatest impact on the bill outcome. The features chosen provided information on the sentence complexity and type of words used (adjective, verb, nouns) for each testimony. Additionally all the testimonies were analyzed to find common phrases used within successful testimonies. Two types of classification models were implemented: ones that used the manually extracted feature as input and ones that used their own feature extraction process. The results from the classification models and feature analysis show that certain aspects within a testimony such as sentence complexity and using specific phrases significantly impact the bill outcome. The most successful models, Support Vector Machine and Multinomial Naive Bayes, achieved an accuracy of 91.79\% and 91.22\% respectively

This thesis intends to evaluate the feasibility of supervised learning models for predicting house prices on the countryside of South Sweden. It is essential for mortgage lenders to have accurate housing valuation algorithms and the current model offered by Booli is not accurate enough when evaluating residence prices on the countryside. Different types of boosted decision trees were implemented to address this issue and their performances were compared to traditional machine learning methods. These different types of supervised learning models were implemented in order to find the best model with regards to relevant evaluation metrics such as root-mean-squared error (RMSE) and mean absolute percentage error (MAPE). The implemented models were ridge regression, lasso regression, random forest, AdaBoost, gradient boosting, CatBoost, XGBoost, and LightGBM. All these models were benchmarked against Booli's current housing valuation algorithms which are based on a k-NN model. The results from this thesis indicated that the LightGBM model is the optimal one as it had the best overall performance with respect to the chosen evaluation metrics. When comparing the LightGBM model to the benchmark, the performance was overall better, the LightGBM model had an RMSE score of 0.330 compared to 0.358 for the Booli model, indicating that there is a potential of using boosted decision trees to improve the predictive accuracy of residence prices on the countryside. / Denna uppsats ämnar utvärdera genomförbarheten hos olika övervakade inlärningsmodeller för att förutse huspriser på landsbygden i Södra Sverige. Det är viktigt för bostadslånsgivare att ha noggranna algoritmer när de värderar bostäder, den nuvarande modellen som Booli erbjuder har dålig precision när det gäller värderingar av bostäder på landsbygden. Olika typer av boostade beslutsträd implementerades för att ta itu med denna fråga och deras prestanda jämfördes med traditionella maskininlärningsmetoder. Dessa olika typer av övervakad inlärningsmodeller implementerades för att hitta den bästa modellen med avseende på relevanta prestationsmått som t.ex. root-mean-squared error (RMSE) och mean absolute percentage error (MAPE). De övervakade inlärningsmodellerna var ridge regression, lasso regression, random forest, AdaBoost, gradient boosting, CatBoost, XGBoost, and LightGBM. Samtliga algoritmers prestanda jämförs med Boolis nuvarande bostadsvärderingsalgoritm, som är baserade på en k-NN modell. Resultatet från denna uppsats visar att LightGBM modellen är den optimala modellen för att värdera husen på landsbygden eftersom den hade den bästa totala prestandan med avseende på de utvalda utvärderingsmetoderna. LightGBM modellen jämfördes med Booli modellen där prestandan av LightGBM modellen var i överlag bättre, där LightGBM modellen hade ett RMSE värde på 0.330 jämfört med Booli modellen som hade ett RMSE värde på 0.358. Vilket indikerar att det finns en potential att använda boostade beslutsträd för att förbättra noggrannheten i förutsägelserna av huspriser på landsbygden.

Research has shown that the benefits of having a populace literate in science are great. Even if citizens are not literate in basic science, it is important that citizens still appreciate science and those with expertise in the field for many reasons. Recent research suggests that the United States (U.S.) has lower levels of science literacy than it should. Evidence may also suggest that many U.S. citizens are not appreciative of science. Overall, little research has been conducted on what may predict science literacy and science appreciation which is the aim of this research. Specifically, I have examined socio-personal variables, beliefs, thought paradigms, and various demographic variables that may be predictive of science literacy and science appreciation. Results indicated that scriptural literalism, religiosity, and magical ideation were predictive of low levels of science literacy. In addition, predictors of low levels of science appreciation included scriptural literalism and magical ideation. Implications of the findings are discussed.

No description available.

No description available.

This diploma thesis deals with the topic of work with illustration in kindergarten as support to understanding a text that is read aloud. The aim is to design and verify methods of working with illustrations in kindergarten that support the understanding of a text that is read aloud to preschool children. In the theoretical part of the thesis, I deal with illustration, visual literacy, method of working with a book, understanding a text (read aloud) and the characteristics of preschool children with regard to the topic of the thesis. Subsequently, I apply this knowledge in the practical implementations. In the practical part, I propose and verify methods of working with illustration in kindergarten which should support the understanding of a text that is read aloud to preschool children. The thesis is focused on qualitative research in which I compared differences between research and experimental group sample. First, I tested the level of skill of all twenty children when working with illustrations and understanding of a text read aloud (both research and experimental group samples). Then I put together seven preparations where I focused on the development of reading strategies and I supported the children in the understanding of the text read aloud on the basis of selected expected outcomes...

Within the educational psychology literature there is an abundance of research in the field of metacognition. The concentration of this research however has been in primary and secondary school contexts with little attention given to tertiary students' understanding or use of metacognition; there has been even less attention to whether age is a factor in tertiary students' perceptions of their metacognitive processes. The primary purpose of this study was to explore the perceptions of two distinct groups of first year university students, towards their understanding and usage of metacognitive processes and strategies. The two groups defined were traditional school leavers and mature age students. The findings from the exploration of these perceptions were compared to ascertain the similarities and differences in metacognitive processes between the two cohorts. The data collected for this study were obtained through a process of individual face-to-face in- depth interviews. The choice of this methodology was deliberate in order to gather rich data about the students' perceptions and experiences rather than attempt to measure their levels of metacognition against some predetermined standard. Data were collected and analyzed on the two constructs of metacognition which were identified in the literature search. These were metacognitive knowledge and metacognitive control. A range of affective variables such as self efficacy, motivation and expectancy of success, which impact on students' metacognitive abilities and processes, were also considered in the data collection and analysis. The findings indicated that age was a factor in determining some differences and similarities in students' perceptions of their own and others metacognitive processes. In certain cases the traditional school leavers' recency of experience with formal study was deemed an advantage; in others the life experience of the mature age students was perceived an advantage. In some instances the age of the student had no discernable impact on their understanding of, and ability to, utilize metacognitive strategies. These findings assist to broaden the understanding of student perceptions of metacognition in the tertiary context. The findings also make it imperative that tertiary institutions make fewer assumptions about the skills and abilities of their commencing students based on the criterion of age and offer more opportunities to assist students to understand the value of developing and improving their metacognitive processes.

No description available.

No description available.