Why this thesis is needed. This thesis is motivated by the falling school results of Swedish 15-year-olds, a lack of IT tools in Swedish schools and a call for turning the theory on formative assessment into practice. Previous research that is used in the thesis. This thesis mainly builds on the research done by John Hattie which is presented in his book Visible Learning from 2009, Wiliam & Thompson's research on effective formative assessment from 2007, Pachler et al. research on formative e-assessment from 2010 and Panadero & Jonsson's research on scoring rubrics from 2013. Research question. What key aspects should be taken into consideration when designing and implementing an IT tool based on scoring rubrics which aims to support formative assessment according to the key strategies for effective formative assessment as identified by Wiliam & Thompson (2007)? Method. To answer this question I chose a qualitative approach of parallel paper prototyping where I iteratively exposed the design and my ideas during focus groups to teachers, a headmaster, employees at The Swedish National Agency for Education and high school students, followed by an evaluation of the key topics that surfaced during the focus groups. Results. The results consist of eight key aspects to take into consideration when designing and implementing such a tool. Discussion and future research. Finally I discuss the implications of my findings and present directions for future research which include the construction of the tool, further investigation of the key aspects identified in this study, identification of additional key aspects and more. / Varför denna avhandling behövs. Denna avhandling motiveras av de fallande skolresultaten hos svenska 15-åringar, en brist på IT-verktyg i svenska skolor och ett rop efter att vända forskning inom formativ bedömning till praktik. Tidigare forskning som används. Denna avhandling bygger i huvudsak på forskningen av John Hattie som presenteras i hans bok Visible Learning från 2009, William & Thompsons forskning på effektiv formativ bedömning från 2007, Pachler et al. forskning på formativ e-bedömning från 2010 och Panadero & Jonssons forskning på betygsmatriser från 2013. Forskningsfråga. Vilka nyckelaspekter ska tas i åtanke vid utveckling av ett IT-verktyg baserat på betygsmatriser som stödjer formativ bedömning enligt de fem nyckelstrategierna för effektiv formativ bedömning som formulerats av Wiliam & Thompson (2007)? Metod. För att besvara denna fråga valdes en kvalitativ metod där parallell pappersprototypning användes under ett antal iterationer av fokusgrupper under vilka designen och tidigare diskussionpunkter diskuterades med lärare, en rektor, anställda vid Skolverket samt gymnasiestudenter. Fokusgrupperna följdes upp med en utvärdering av de största diskussionpunkterna som dök upp. Resultat. Resultaten består av åtta nyckelaspekter att ha i åtanke vid utveckling av ett sådant verktyg. Diskussion och framtida forskning. Slutligen diskuteras implikationerna av resultaten och direktioner för framtida forskning framförs. Dessa direktioner inkluderar utveckling av verktyget, vidare utredning av de nyckelaspekter som hittats i denna avhandling, identifikation av ytterligare nyckelaspekter och mer.

No description available.

This thesis has investigated two-stage regularized logistic regressions applied on the credit scoring problem. Credit scoring refers to the practice of estimating the probability that a customer will default if given credit. The data was supplied by Klarna AB, and contains a larger number of observations than many other research papers on credit scoring. In this thesis, a two-stage regression refers to two staged regressions were the some kind of information from the first regression is used in the second regression to improve the overall performance. In the best performing models, the first stage was trained on alternative labels, payment status at earlier dates than the conventional. The predictions were then used as input to, or to segment, the second stage. This gave a gini increase of approximately 0.01. Using conventional scorecutoffs or distance to a decision boundary to segment the population did not improve performance. / Denna uppsats har undersökt tvåstegs regulariserade logistiska regressioner för att estimera credit score hos konsumenter. Credit score är ett mått på kreditvärdighet och mäter sannolikheten att en person inte betalar tillbaka sin kredit. Data kommer från Klarna AB och innehåller fler observationer än mycket annan forskning om kreditvärdighet. Med tvåstegsregressioner menas i denna uppsats en regressionsmodell bestående av två steg där information från det första steget används i det andra steget för att förbättra den totala prestandan. De bäst presterande modellerna använder i det första steget en alternativ förklaringsvariabel, betalningsstatus vid en tidigare tidpunkt än den konventionella, för att segmentera eller som variabel i det andra steget. Detta gav en giniökning på approximativt 0,01. Användandet av enklare segmenteringsmetoder så som score-gränser eller avstånd till en beslutsgräns visade sig inte förbättra prestandan.

Radiologists often have to look through many different patients and examinations in quick succession, and to aid in the workflow the different types of images should be presented for the radiologist in the same manner and order between each new examination. Thus decreasing the time needed for the radiologist to either find the correct image or rearrange the images to their liking. A step in thisprocess requires a comparison between two images to be made and produce a score between 0-1 describing how similar the images are. A similar algorithm already exists at Sectra, but that algorithm only uses the metadata from the images without considering the actual pixel data. The aim of this thesis were to explore different methods of doing the same comparison as the previous algorithm but only using the pixel data. Considering only 3D volumes from CT examinations of the abdomen and thorax region, this thesis explores the possibility of using SSIM, SIFT and SIFT together with a histogram comparison using the Bhattacharyya distance for this task. It was deemed very important that the ranking produced when ordering the images in terms of similarity to one reference image followed a specific order. This order was determined by consulting personnel at Sectra that works closely with the clinical side of radiology. SSIM were able to differentiate between different plane orientations since they usually had large resolution differences in each led, but it could not be made tofollow the desired ranking and was thus disregarded as a reliable option for this problem. The method using SIFT followed the desired ranking better, but struggled a lot with differentiating between the different contrast phases. A histogram component were also added to this method, which increased the accuracy and improved the ranking. Although, further development is still needed for thismethod to be a reliable option that could be used in a clinical setting.

This dissertation contributes to the ongoing debate on how educational inputs make a difference and how to allocate them efficiently. Educational inputs could be broadly defined as any personnel inputs such as teachers and career service staff, learning environment that includes peers and school facilities, and policies that facilitate learning. This dissertation explores three topics: peer effects in higher education, the consequences of college expansion, and the impacts of school closures. Chapter I estimates the peer effects of non-cognitive skills. I show how peers’ non-cognitive skills influence students' academic outcomes and own non-cognitive skills. I use a unique dataset that includes information on student non-cognitive skills, course grades, and friendship from a university in China that randomly assigns students to dormitories. My first main finding is that peers’ non-cognitive skills affect students’ academic outcomes positively but differentially. All students benefit from exposure to “persistent” peers, while students with low baseline academic ability also benefit from exposure to “motivated” peers. My second main finding is that peers also affect the development of students’ self-control and willingness to socialize. These findings have important implications in evaluating the social returns to interventions that improve non-cognitive skills and education policies that change peer group composition. Chapter II summarizes the current literature on college expansions, which change the education resource for many students. Studies have explored the impact of College Expansions that happened worldwide and this chapter summarizes literature in the field of economics of education. This chapter pays special attention to studies that explore the impact on wages and employment and how current studies identify causal relationships. Meanwhile, this chapter reviews how current studies examine the impacts of college expansion in China starting from 1999, which was unparalleled in magnitude. Finally, I discuss how future studies could improve to identify causal effects of the impact of the tremendous college expansion in China. Chapter III, a joint work with Ying Xu, estimates the effect of school closures causedby wildfires. School closures are a common and disruptive feature of education systems when sudden shocks from weather, natural disasters, or infectious disease require that students remain at home rather than in the classroom. Indeed, since January 2020, school closures have happened all around the world due to the COVID-19 pandemic. In the United States, more than 50 million students are currently out of school due to COVID-related closures. This raises an important question: How do sudden school closures affect student development in the short and medium term? In this chapter, we use administrative data to examine the causal effect of unexpected school closures, exploiting sudden variations in these closures due to wildfires in California. We show that unexpected closures have negative effects on student test scores, and the loss of school time is one of the most important mechanisms of decline in student achievement. Meanwhile, minority students and students from school districts with low socioeconomic status experience larger negative effects from such unexpected closures. We argue that these results can help inform policy to identify and address the negative impacts of such closures.

Despite being governed by several laws aimed at preventing racial inequality in access to housing and credit resources, the mortgage lending market remains a contributor to racial and place-based disparities in homeownership rates, wealth, and access to high-quality community resources. Scholarship has identified persistent disparities in mortgage loan approval rates and subprime lending between white borrowers and those from other racial and ethnic groups, and between white neighborhoods and neighborhoods with high levels of non-white residents. Against this backdrop, the mortgage lending industry is undergoing rapid, technology-driven changes in risk assessment and application processing. Traditional borrower risk-assessment methods including face-to-face discussions between lenders and applicants and the prominent use of FICO credit scores have been replaced or supplemented by automated decision-making tools at a new generation of institutions known as fintech lenders. Little is known about the relationship between lenders using these new tools and the racial and spatial disparities that have long defined the wider mortgage market. Given the well-documented history of discrimination in lending along with findings of technology-driven racial inequality in other economic sectors, fintech lending’s potential for racial discrimination warrants increased scrutiny. This dissertation compares the lending outcomes of traditional and fintech mortgage lenders in the United States depending on applicant and neighborhood racial characteristics. Using data from the Home Mortgage Disclosure Act, an original dataset classifying lenders as fintech or traditional, and an array of complimentary administrative data sources, it provides an assessment of the salience of race and place in the rates at which mortgage loans from each lender type are approved and assigned subprime terms. Results from a series of regression-based quantitative analyses suggest fintech mortgage lenders, like traditional mortgage lenders, approve and deny loans and distribute subprime credit at disparate rates to white borrowers and neighborhoods relative to nonwhite borrowers and neighborhoods. Findings suggest that policymakers and regulators must increase their oversight of fintech lenders, ensuring that further advances in lending technology do not concretize longstanding racial and spatial disparities.

An ongoing challenge for most businesses is to filter out potential customers from their audience. This thesis proposes a method that takes advantage of user data to classify po- tential customers from random visitors to a website. The method is based on the Predictive Lead Scoring method that segments customers based on their likelihood of purchasing a product. Our method, however, aims to predict user conversion, that is predicting whether a user has the potential to become a customer or not. Six supervised machine learning models have been used to carry out the classifica- tion task. To account for the high imbalance in the input data, multiple resampling meth- ods have been applied to the training data. The combination of classifier and resampling method with the highest average precision score has been selected as the best model. In addition, this thesis tries to quantify the effect of feature weights by evaluating some feature ranking and weighting schemes. Using the schemes, several sets of weights have been produced and evaluated by training a KNN classifier on the weighted features. The change in average precision obtained from the original KNN (without weighting) is used as the reference for measuring the performance of ranking and weighting schemes.

The purpose of this research was to investigate possible examiner bias in scoring the Verbal subtests of the Wechsler Intelligence Scale for Children (Revised) due to the level of facial attractiveness of the child. Sex of the child and sex of the research subject were also included as independent variables. No main effect for attractiveness or sex x attractiveness interactions were found. Thus, little evidence emerged to suggest attractiveness stereotyping effects in an intelligence testing context. However, female children received significantly higher Comprehension and total Verbal scores than did male children. In addition, while male subjects did not provide differential Verbal scores for male and female children, female subjects tended to be biased toward female children, regardless of attractiveness level. A secondary goal of this study was to determine if the research subjects differentially attributed positive characteristics to attractive versus unattractive children. Indeed, it was empirically established that, in this testing environment, adults attributed more positive personality and social characteristics to attractive than unattractive children. Implications for clinicians/diagnosticians are discussed. It is suggested that future research attempt to delineate a continuum of diagnostic measures wherein one pole represents objective measures with little risk of bias and the other pole is the extreme of subjective instruments with high resk of examiner bias.

Indiana University-Purdue University Indianapolis (IUPUI) / Authorship attribution (AA) is the process of identifying the author of a given text and from the machine learning perspective, it can be seen as a classification problem. In the literature, there are a lot of classification methods for which feature extraction techniques are conducted. In this thesis, we explore information retrieval techniques such as Word2Vec, paragraph2vec, and other useful feature selection and extraction techniques for a given text with different classifiers. We have performed experiments on novels that are extracted from GDELT database by using different features such as bag of words, n-grams or newly developed techniques like Word2Vec. To improve our success rate, we have combined some useful features some of which are diversity measure of text, bag of words, bigrams, specific words that are written differently between English and American authors. Support vector machine classifiers with nu-SVC type is observed to give best success rates on the stacked useful feature set. The main purpose of this work is to lay the foundations of feature extraction techniques in AA. These are lexical, character-level, syntactic, semantic, application specific features. We also have aimed to offer a new data resource for the author attribution research community and demonstrate how it can be used to extract features as in any kind of AA problem. The dataset we have introduced consists of works of Victorian era authors and the main feature extraction techniques are shown with exemplary code snippets for audiences in different knowledge domains. Feature extraction approaches and implementation with different classifiers are employed in simple ways such that it would also serve as a beginner step to AA. Some feature extraction techniques introduced in this work are also meant to be employed in different NLP tasks such as sentiment analysis with Word2Vec or text summarization. Using the introduced NLP tasks and feature extraction techniques one can start to implement them on our dataset. We have also introduced several methods to implement extracted features in different methodologies such as feature stack engineering with different classifiers, or using Word2Vec to create sentence level vectors.

No description available.