Math skills are essential to daily life, impacting a person?s ability to function at home, work, and in the community. Although reading has been the focus in recent years, many students struggle in math. The inability to master math calculation and problem solving has contributed to the rising incidence of student failure, referrals for special education evaluations, and dropout rates. Studies have shown that curriculum based measurement (CBM) is a well-established tool for formative assessment, and could potentially be used for other purposes such as a prediction of state standards test scores, however to date there are limited validity studies between mathematics CBM and standard-based assessment. This research examined a brief assessment that reported to be aligned to national curriculum standards in order to predict student performance on state standards-based mathematics curriculum, identify students at-risk of failure, and plan instruction. Evidence was gathered on the System to Enhance Educational Performance Grade 3 Focal Mathematics Assessment Instrument (STEEP3M) as a formative, universal screener. Using a sample of 337 students and 22 instructional staff, four qualities of the STEEP3M were examined: a) internal consistency and criterion related validity (concurrent); b) screening students for a multi-tiered decision-making process; c) utility for instructional planning and intervention recommendations; and d) efficiency of administration, scoring, and reporting results which were the basis of the four research questions for this study. Several optimized solutions were generated from Receiver Operator Curve (ROC) statistical analysis; however none demonstrated that the STEEP3M maximized either sensitivity or specificity. In semi-structured interviews teachers reported that they would consider using the STEEP3M, however only as a part of a decision-making rubric along with other measures. Further, teachers indicated that lessons are developed before the school year starts, more in response to the sequence of the state standards than to students? needs. While the STEEP3M was sufficiently long enough for high-stakes or criterion-referenced decisions, this study found that the test does not provide sufficient diagnostic information for multi-tiered decision-making for intervention or instructional planning. Although practical and efficient to administer, the conclusions of this study show the test does not provide sufficient information on the content domain and does not accurately classify students in need of assistance.

BACKGROUND: Quantitative evidence of direct links between dietary intake during pregnancy and maternal and infant outcomes in the preterm population is lacking in the literature. However, studies are starting to investigate relationships between them, and data shows that many of the adverse effects of poor maternal diet are linked to inflammatory response and dysbiosis of the microbiome in both the mother and her offspring. OBJECTIVE: Our objective with this study was to analyze the relationships between sociodemographic factors, maternal diet, pH of breast milk, and infant inflammation. We aimed to assess the dietary pattern of our population of mothers who delivered preterm in comparison to other populations, as well as explore the possibility of using pH of mother’s own milk in future research. METHODS: We reviewed the Electronic Medical Records (EMRs) of participants to gather clinical and demographic characteristics (infants n = 53; mothers n = 45). Maternal participants also completed the Dietary Screener Questionnaire (DSQ). Spearman’s rank correlation and raw unadjusted linear regression analyses were used to investigate relationships between maternal diet and characteristics, mother’s own milk pH, and infant urinary intestinal fatty acid binding protein (I-FABP). Kruskal Wallis analysis was used to analyze between group differences of maternal comorbidities. RESULTS: None of our maternal participants met the guidelines for dietary recommendations by the United States Department of Agriculture (USDA) for pregnant women. This follows the trend in national data for women who are pregnant. The greatest number of women met the recommendation for fiber intake (n = 12). Maternal intake of fiber and whole grains was negatively correlated with pH of mother’s own milk (p <0.5). We did not find any significant correlations between maternal characteristics and maternal diet or pH of mother’s own milk. However, meeting the guidelines for added sugars differed by race (p = 0.03). We found no statistically significant correlations between urinary I-FABP and pH of mother’s own milk or maternal dietary intake. Urinary I-FABP values differed by infant sex (p = 0.03) and infant feeding status (> 50% formula or donor milk vs. < 50% formula or donor milk, p = 0.03). Analysis by groups showed statistically significant differences (p = 0.04). Preeclamptic participants had a higher intake of whole grains (0.97 oz) than women without preeclampsia (0.69 oz). Mothers with gestational diabetes had a lower intake of sugar (14.4 tsp) compared to women without diabetes (17.9 tsp) (p = 0.01). We found no other statistically significant results between groups for maternal diet, milk pH, or urinary I-FABP. CONCLUSION: Our findings suggest that additional research on mother’s own milk pH may be warranted, and that continued education on the importance of a healthy diet and its benefits during pregnancy is needed. Areas of planned future research include fat intake calculations and inflammatory measures of the maternal dietary data.

The current study was completed through a retrospective analysis of school records of elementary school students in the Northeast Region of the Philadelphia School District (PSD) who have participated in the Fast ForWord (FFW) Language program. The data requested from student records included: demographic information (e.g., gender, grade,age, ethnicity, disability, and special education status), DIBELS scores, and FFW completion and participation variables. The current study set out to determine if DIBELS scores can predict student performance or mastery level on the FFW program. A total of seven individual FFW variables (percent of completion for each activity) and five overall FFW variables (percent complete, participation level, attendance level, total days to complete, and successful performance) served as the outcome variables. Frequency distributions, Pearson correlations, an ANOVA, and a standard multiple regression were used to determine the relationships of demographic variables among predictor and outcome variables as well as the predictive power of the DIBELS test scores. Results of the standard multiple regression analysis failed to yield significant results in the ability for either DIBELS raw or benchmark scores to predict performance on the FFW reading program. The current study highlighted that in the real-life conditions of a large, urban public school system, DIBELS seems to have very little, if any, predictive abilities specific to designating students appropriately to an intensive, costly, and time-consuming intervention program. It is hoped that the information presented in this study will stimulate some positive discussion and changes in the assessment and referral processes currently being widely employed across American schools in order to better serve and educate American children that demonstrate symptoms of early reading deficits. / School Psychology

Universal school-wide social-emotional screeners identify at-risk students with social-emotional behavior problems (Romer et al., 2020). Identifying such students alone cannot prevent these social, emotional, and behavioral concerns from becoming problematic. However, data gathered from social-emotional screening can guide the development of strategies, supports, interventions, and progress monitoring students at risk across all tiers (Yates et al., 2008), leading to the prevention of social-emotional and behavioral problems from turning maladaptive (Humphrey & Wigelsworth, 2016; Walker et al., 2004). A school district in the Mountain West area of the United States developed a screening survey that addresses school climate, culture, and connectedness to administer to their student body. Their survey aims to identify students' needs in the following character social-emotional learning (SEL) skills that contribute to student well-being: self-awareness, compassion, resilience, and respect. Their screening survey can potentially identify the general student body's social-emotional behavioral needs. The data gathered may be used to create tiered supports that address students' needs. In this study, we conducted two focus groups that provided a forum for school psychologists in this Mountain West school district who work in an elementary school to discuss what tiered supports may be appropriate to implement in response to needs identified by the survey. The discussion also included professional development topics the participants perceived necessary for school teams responding to the survey data. The findings from this study contribute to the existing literature by recognizing that school psychologists may understand implementation science and can help lay the needed groundwork before implementing social-emotional screeners so that the process can be more efficient and effective. The findings emphasize the understanding that from the perspective of school psychologists, social-emotional learning should be applied universally and collaboratively at school and at home. School teams need to allocate time to teach social-emotional learning at school. The findings also suggest that school psychologists can determine appropriate interventions if screening data suggest a weakness in social-emotional and behavioral areas and that data collected from the screener can be used to guide topics for professional development.

This study examines the Swedish Stock Exchange during the time period of 1998-2016. Where the purpose is to investigate and compare five different investment strategies to see if these investment strategies can create excess return on their investments, after adjustment for risk. The investment strategies can be found on the internet, and be used after purchasing a smaller amount of money, therefore the results can be applied to all investors independent on their level of experience. The results for the different investment strategies are not clear, the different tests give mixed results which leaves four of five hypotheses unanswered. However, there is one strategy that can be rejected, it cannot beat the market, which is the Net-Nets strategy. In general, one could thus say that the investment strategies can create higher return compared to the market, but that these returns are random. Therefore, it requires a longer time period for the investor as well as higher risk, since one never knows when this large return will be given.

The purpose of this quantitative study was to explore a possible relationship between remote learning with 1:1 devices and reading performance for third graders in a school district in Tennessee by comparing differences between universal screener reading data from fall 2020 and universal screener reading data from both fall 2018 and fall 2019. Star Reading universal screener Normal Curve Equivalent (NCE) data from fall 2018, fall 2019, and fall 2020 from three elementary schools were used for the study. Results indicated that the third-grade students’ fall 2020 Star Reading universal screener NCEs were significantly lower than their fall 2018 first grade and fall 2019 second grade universal screener NCEs. The fall 2020 third-grade NCEs were not significantly different from the third-grade fall 2018 and fall 2019 NCEs. There were significant differences in reading performance by gender. The third-grade female students scored significantly lower on their fall 2020 Star Reading universal screener than on their first-grade fall 2018 and second-grade fall 2019 Star Reading universal screener. There were no significant differences in the Star Reading universal screener NCEs for the third-grade male students. Implications for practice and recommendations for further research are discussed.

Much research has been done on the use of machine learning to extract features such as buildings, lakes et cetera from satellite imagery, and while this dataset is valuable for many use cases, it is limited to time periods in which satellites were used. Historical maps have a much greater range of available time periods but the viability of using machine learning to extract data from these has not been investigated to any great extent. This case study uses a real-world use case to show the efficacy of using a U-Net convolutional neural network to extract features drawn on hand-drawn maps. By implementing a random forest as a pre-screener to the U-Net the goal was to filter out noise that could lead to false positives. By filtering out the noise the hope was to increase the accuracy of the U-Net. The pre-screener in this study has not performed well on the dataset and has not improved the performance of the U-Net. The U-Nets ability to extrapolate the location of features not explicitly drawn on the map was not clearly established. The results of this study show that the U-Net CNN could be an invaluable tool for quickly extracting data from this typically cumbersome data source, allowing for easier access to a wealth of data. The fields of archeology and climate science would find this especially useful.

This graduation thesis is exploring using of Information and Communication Technologies in the course of analysis of capital markets. The basic financial theory is described at the begging of the thesis. After this we investigate information dilemmas. Expansion of information science allowed modern stock exchanges to change their traditional trading model to electronically trading model, which is much cheaper and faster. Stock exchange trading systems, clearing centers, depositors of securities and trading platforms are described in this thesis. These technologies allow us to automate a process of trading. Nowadays the modern stock exchange is a provider of high-quality IT services. In addition, IT offers a number of tools for analysis of capital markets. These tools are applications for technical and fundamental analyses. Stock screeners allowed us to filter a big set of securities. It is the most usable analysis technology. In the last section of the thesis the technical stock screener which allowed identifying market opportunities was created. We used Microsoft Visual Studio 2008 and C# the programming language. We found a volume indicator which allowed us to achieve above-average gain 5% during the analysis of the big set of market data.

Plusieurs études à ce jour démontrent que la consommation d’aliments ultra-transformés (AUT) serait associée à une alimentation de faible qualité et à une augmentation du risque de maladies chroniques non-transmissibles (MCNT) et d’obésité. Actuellement, pour les adultes canadiens, il n’existe encore aucun questionnaire permettant de discriminer rapidement entre différents profils de consommateurs d’AUT. Ce mémoire présente donc un projet pilote de développement et de validation initiale d’un Outil pour mesurer la consommation de Produits Ultra-Transformés (le OUTPUT). Pour ce faire, les catégories d’AUT contribuant à 0,5% et plus de l’apport énergétique total des adultes canadiens ont été identifiées d’après les données de consommation nationale (ESCC) de 2015. Ces catégories ont été représentées par des photos d’AUT vendus en épicerie et en restauration rapide. Celles-ci constituaient les deux questionnaires alimentaires simplifiés développés (rappel de 24 heures et questionnaire de fréquence). La validité apparente et de contenu du OUTPUT a été évaluée par un comité d’experts, avec la méthode Delphi. Deux tours d’enquête ont été nécessaires pour observer un consensus (fixé à 80% et plus) quant aux dimensions évaluées. La validité apparente de l’outil a ensuite été évaluée par 11 membres de la communauté de l’UdeM. Les données recueillies à travers cette étude pilote ont permis d’émettre des recommandations pour l’étude future de développement et de validation de l’outil. Une fois validé, celui-ci permettra aux nutritionnistes, aux chercheurs en épidémiologie et aux décideurs politiques une meilleure identification, gestion et prévention des MCNT et d’obésité, en lien avec la consommation d’AUT. / Several studies to date have shown that ultra-processed food consumption (UPF) is associated with low diet quality and increased risk of non-communicable diseases (NCDs) and obesity. Currently, there is no rapid screener to estimate UPF consumption based on Canadian dietary patterns. This research presents a pilot study that addresses the early stages of the development and validation of a screener to this purpose (the OUTPUT). Ultra-processed food categories contributing to 0.5% or more of the total energy intake for Canadian adults (18 years and older) were identified based on 2015 national consumption data (CCHS). Thus, two simplified dietary screeners were developed (a 24-hour recall and a food frequency questionnaire). The face and content validity of the OUTPUT was evaluated by an expert advisory committee, through the Delphi method. Two rounds were required to observe an agreement (set at 80% or more) regarding the different metrics that were assessed. The face validity of the screener was then evaluated by 11 participants from the UdeM community. The data collected through this pilot study made it possible to provide recommendations for the future study in which the development and validation of the tool will take place. Once validated, this tool will enable nutritionists, epidemiology researchers, and policymakers to better identify, manage and prevent NCDs and obesity.

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