sessment of Educational **Achievement** (NAEA), public reporting of school performance on the website, and financial incentives linked to school performance for the purpose of equity enhancement (Lee, 2017). The proportion of students who do not reach a basic proficiency level in Korean, English and **math** at the school level was publicly posted on a website in Korea as of 2012. On the contrary, Koretz (2017) criticizes high-stakes test-based accountability, discuss- ing negative effects such as inappropriate test preparation, score inflation, corruption of ideal of teaching, and widespread cheating. According to him, the most substantial positive effect of test-based accountability has improved **math** performance, although it does not persist long. In spite of accumulated evidence about the overall negative effects of test-based accountability, predominantly found in the U.S., the rationale of many other countries’ adopting test-based ac- countability could be ensuring educational equity, of which evidence is found in this research. There are still several limitations in the current research. First, the research used cross- sectional data to examine the multilevel relations between system level external accountability, schools’ internal accountability, and student **math** **achievement**. Therefore, the results do not represent causal relations between explanatory variables and **math** **achievement** and are open to reversed explanations. Some countries with a strong accountability system may have adopted accountability policy measures to address the low level of student **achievement**. As it is impos- sible to determine the causal impact of external accountability on student **achievement** with cross-sectional data, use of longitudinal data is a research direction forward.

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students with intellectual disability. It is important to note that special education preschool program was not superior to the regular education program for children without disabilities. However, this finding is still in favor of the quality of special education preschool program as the earlier studies have found different developmental trajectories in **math** **achievement** between children with and without disabilities, with children without disabilities having higher gains over the course of six months (Hojnoski, Caskie, & Miller Young, 2018). Regular preschool programs, although somewhat inferior to the special education program for children with intellectual disability, have also resulted in large and significant improvements. Given the size of the improvement, it is evident that preschool programs play a huge role in increasing children’s academic competencies. Preschool benefits have been widely reported in the scientific literature and there should be wider public campaigns to enroll more children in Serbia in preschool institutions. The age of start of mandatory program should probably be lowered so even children as young as 4 years could attend free preschool programs, as the evidence suggests that starting preschool at that age can produce benefits decades later (Melhuish, 2011).

The purpose of this quasi-experimental design study was to examine the effect of expressive writing on **math** anxiety and **math** **achievement** for second-grade students with self- reported, high **math** anxiety (HMA). Specifically, students were assessed for **math** anxiety using the **Math** Anxiety Scale for Young Children-Revised (MASYC-R) to determine which students were HMA, moderately **math**-anxious, or low **math**-anxious. A **math** **achievement** assessment (an i-Ready computational fluency assessment) was used to assess their baseline computation skills. Students were asked to write expressively about mathematics concerns (treatment) or write expressively about a free-choice topic (control). Following five 10-minute sessions of writing, students were reassessed on **math** **achievement** and self-reported **math** anxiety. This research has potential positive implications for students with HMA, teachers, principals, and the school district.

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In parallel with the literature, the present study demonstrated that the teacher morale and motivation generally has a significant role on students’ science and **math** **achievement** in all countries. Although there are some differences between the results of TIMSS and PISA applications for some countries, it was found that the teacher morale and motivation generally has a positive effect according to both results. There are similar results in the literature. For example, Korur [12] studied the effects of the qualities of sciences teachers on the student **achievement**, attitude and motivation throughout Turkey and he found that the pedagogical content knowledge, lesson preparedness and personal traits/motivations of teachers were the variables with the highest effect on the physics **achievement**, attitude and motivation of the students.

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the model includes four variables pertaining to human resources at the school level. Again there is one variable, index of teacher morale (TCMORALE), having a positive effect on students’ **math** **achievement**. The coefficient for TCMORALE indicates that a one unit increase in this index is associated with an increase in **achievement** of about 9 points on **math** literacy scores. As noted earlier in this study TCMORALE has a standard deviation of 0.99, which means a one standard deviation increase in this variable is associated with a 9 points of increase in student scores. Surprisingly, other teacher-related factors, school climate and teacher short- age, identified as significant predictors of **achievement** by many previous research appears to have no significant effect on mathematical literacy **achievement** in the present study. This result emphasizes the priority of the teacher morale in education.

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Whereas working memory capacity is consistently and robustly correlated with **math** **achievement**, short-term memory capacities appear to be related to **math** abilities in only some contexts and often do not account for unique variance in **math** **achievement** when controlling for other factors such as reading comprehension and fluid intelligence (Bull & Johnston, 1997; Floyd et al., 2003; Swanson & Jerman, 2006). In fact, some researchers have concluded that it is the central executive or central executive processes that drive the robust relationship between working memory and **math** **achievement** (Dark & Benbow, 1990, 1991; Jarvis & Gathercole, 2003; Swanson & Jerman, 2006). For example, a meta-analysis of cognitive predictors of **math** disabilities revealed that only verbal working memory capacity was a significant predictor of effect sizes (i.e., group differences in **math** performance between children diagnosed with **math** disabilities and those identified as average **math** achievers) when controlling for other factors. These results were based on a regression model that included, among other cognitive variables, verbal short-term memory (STM) span (i.e., phonological loop capacity) and visual- spatial working memory capacity (Swanson & Jerman, 2006). The authors concluded that deficits in “controlled attention to verbal information (i.e., with the influence of other variables such as STM partialed out) was a defining feature of MD children when compared with average achievers” (p. 269).

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The variable of sex appears to be linked to two of the academic subjects, and in different ways. Where Language is concerned, the female students obtained better results, while on the contrary, in social science, being male predicted a higher probability of having good academic results. This data confirms a tendency about Language found in other studies on education (OECD, 2001; INECSE, 2001, 2003; Van Damme, De Fraine, Van Landeghem, Opnedakker, 2002). Nevertheless, the data does not coincide with the results of other research on **Math** **achievement**; the majority of research has emphasized male students’ performance in **Math** (OECD, 2004, INCE, 1997). However, it is important to remember that in the Basque Country and Castilla and Leon samples of other studies, no differences were found between the sexes in **math**, which indeed confirms our results. Last, in the case of Social Science, our data did not coincide with that of the INECSE study (2001, 2003) either, where male students obtained significantly lower scores than their female counterparts. One possible explanation for these results could be the type of test used, which emphasized procedural content, not just the concepts, which could perhaps begin to approach difficult questions and forge a closer understanding of what are traditionally considered “areas of science.”

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Arithmetic ability could also be another predictor of **math** **achievement**. Arithmetic ability includes the skills such as manipulating mathematical knowledge and concepts in ways that transform their meaning and implications. It allows students to interpret, analyze, synthesize, generalize, or hypothesize the facts and ideas of mathematics. Students with high arithmetic ability or mathematical reasoning can engage in tasks such as solving complex problems, discovering new meanings and understanding, and arriving at logical conclusions. Arithmetic ability was determined by various studies as a critical factor on students’ **math** **achievement**. For instance, in a study by Kaeley (1993), arithmetic ability gave the highest correlation coefficient with mathematics **achievement**. Similarly, student **achievement** scores were found to be most strongly predicted by level of ability (Schiefele and Csikszentmihalyi, 1995). Some other researchers have also investigated the relationship of gender issues and arithmetic ability on **math** **achievement**. For instance, Mills (1997) conducted a study to investigate longitudinal data gathered over 10 years with an aim at asking whether personality traits were related to gender differences in long-term **achievement** in mathematics and the sciences. The study revealed that **math** ability was the most significant predictor of long-term **achievement** in **math** for young women. However, the level of **math** ability did not seem to be a factor of long-term **math** **achievement** for young men.

If students completed all 44 problems before the minute, they stood at their place. The researcher then corrected the first part of the sprint worksheet with the class by reading the correct answers at a quick pace. Students were directed to shout “yes” and punch out after each response they answered correctly, circle incorrect answers, and then write the number they answered correctly at the top of their worksheet. The researcher asked students to raise their hands if they had one or more correct, two or more correct, three or more correct etc. until no one raised their hand any more. Within these small groups, the student who had the most correct was acknowledged. The researcher then gave students the opportunity to complete the remaining problems. Before moving to part two of the Sprint, the researcher involved students in some form of calisthenics such as jumping jacks, stretching exercises, or jogging in place. While students physically exercised they also participated in mental **math** exercises such as counting by multiples of fours, counting backwards from 50 by fives, skip counting from one inch to the smallest part of an inch. These combination physical and mental **math** exercises lasted for about one minute. Students returned to their seats and prepared to take the second part of the Sprint. Students had 60 seconds to complete this Sprint as well. The researcher corrected this part with the students following the same routine as in Part A and had them mark the number of correct answers at the top of their worksheet. Students also wrote how many more they had correct in the second part from the first part. The researcher asked the students to raise their hands if they improved by one or more, two or more, three or more etc. until no hands were raised. **Math** **achievement** was based on how many more questions each student answered correctly on the second part. The student who had the greatest improvement from Part A to Part B was acknowledged.

Both doing arithmetic and reading involve complex cognitive processes, and there are similarities in the learning processes in arithmetic and reading. Comparison studies of the performance of students with learning problems in mathematics and reading respectively may answer the question whether the difficulties in these two areas are two sides of the same coin. At The National Centre for Reading Education and Research, which is attached to the University of Stavanger, Reikerås (2007) examined aspects of arithmetical performance related to reading performance in several studies. One study investigated possible differences in the performance on simple arithmetic word problems between groups of students with different levels of **achievement** in mathematics and reading. Although word problems involved reading, the findings indicated that level of reading **achievement** was not related to the performance on word problems as much as generel mathematical **achievement** level, as far as solving consistent problems were concerned. In a second study focus was on arithmetical problems without text, both in basic calculation facts and in multi-step calculations. The aim of the study was to investigate how the performance on these two types of arithmetical tasks was related to **achievement** levels in reading and mathematics. The findings indicate that, for children with normal and low general mathematical **achievement** level, the level of reading **achievement** only to a small extent was related to the students’ development of arithmetic performance

METHODS: Using physical activity to teach **math** and spelling lessons was studied in a cluster- randomized controlled trial. Participants were 499 children (mean age 8.1 years) from second- and third-grade classes of 12 elementary schools. At each school, a second- and third-grade class were randomly assigned to the intervention or control group. The intervention group participated in F&V lessons for 2 years, 22 weeks per year, 3 times a week. The control group participated in regular classroom lessons. Children’s academic **achievement** was measured before the intervention started and after the first and second intervention years. Academic **achievement** was measured by 2 mathematics tests (speed and general **math** skills) and 2 language tests (reading and spelling).

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Partitioning variance has been used extensively in educational research as a tool to determine possible sources of school-to-school variation. This paper contributes new insight by assessing if and why there is significant variation in standardized testing performance for entire populations of cohorts within all elementary schools in a single urban school district. Specifically, this study evaluates variance in SAT9 reading and **math** scores over four academic years and within three analytical levels of the educational experience – student, classroom, and school. To do so, this study employs three-level hierarchical linear modeling (HLM) to determine how the overall variance in testing performance can be partitioned within classrooms, between classrooms, and between schools. The initial results indicate that the overwhelmingly most significant contributor to total variance in **achievement** is within classrooms at the student level. However, incorporating variables into a three-tiered model of student **achievement** explains the majority of the between classroom and between school variance, though only half of the within classroom variance.

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Using a unique repeated cross-sections dataset obtained from the Southern and East African Consortium for Monitoring Education Quality (SACMEQ), this paper applies a before and after difference in differences (DIDs) approach to analyze the grade six pupil-level learning impacts of FPE for both Reading and **Math**. By assuming a common trend and relying on the conditional independence assumption, we utilize private schools as a comparison group since these were not directly treated by the FPE intervention. The study finds that the FPE intervention was associated with considerable test score declines for both subjects for pupils enrolled in public schools, especially for boys and in urban schools. Specifically, FPE was associated with reading and **math** test score declines of 0.415 standard deviations (SDs) and 0.510 SDs respectively. Arising from possible competition for pupils, positive spillover effects were observed for elite private schools in urban areas – their **math** test scores improved significantly by 0.384 SDs. The pathway analyses for the observed pupil test score changes in Kenya suggest considerable importance of teachers’ effort s 3 . Teacher efforts significantly

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The interface of Wolfram Alpha is probably the clos- est to ours. However their system is limited to han- dling mostly number queries, and very simple arith- metic problems. In contrast, our system can solve complicated arithmetic problems described by mul- tiple sentences and requiring multiple operations. There has also been a lot of work in quantitative rea- soning. Roy et al. (2015) looks at understanding en- tailment relations among expressions of quantities in text. There has also been efforts to automatically solve school level **math** word problems. Hosseini et al. (2014) looks at solving elementary addition sub- traction problems, Roy et al. (2015) aims to solve single operation problems and Koncel-Kedziorski et al. (2016) solves single equation problems. The system of Shi et al. (2015) tackles number word

Work can be done regarding the relationship or predictiveness of preschool programming types and the scores earned on the Kindergarten Assessment given in Oregon. The correlation between outcomes on the Kindergarten Assessment and the SBAC test of **achievement** can be studied to determine if initial levels of school readiness affect academic **achievement** in the early elementary grades. Programmatic quality can be studied to determine which programs produce a holistic Kindergarten readiness outcome. Each individual program type, in particular, In-Home Care, ought to be investigated for measures of quality, as well to investigate how mathematic instruction occurs in these settings. Investigating the methods and frequency of professional development for ECE providers would prove insightful. Qualitative studies can be completed to determine who is not participating in the QRIS, and why they have chosen not to do so. An alternate qualitative study might examine who self-selects into which preschool program type and their rationale for doing so.

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LPTs Developmental delays, lower IQ, lower SR: Infants born 34 – 36 wk gestation (LPT) have higher rates of cerebral palsy, higher rates of mental retardation, lower IQ scores, lower reading and **math** proficiency at school entry, and overall more teacher-reported behavior problems compared with term infants. ECLS-B study revealed that LPTs who are developmentally delayed at 24 mo have increased odds of impairment at school age (5 y). In multivariable analysis, LPTs had higher odds of worse total SR scores (adjusted odds ratio 1.52 [95% confidence interval 1.06 – 2.18]; P = .02). Many improved their performance by age 5 y; those less likely to improve by 5 y were of low SES, had primary language other than English, were black, and had low maternal education. a

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From the results it appears that, in this particular botany course, one learning style does not have a significant advantage over another one. Even though research has shown that typically, in science and **math** courses, students with AS and CS learning styles have higher **achievement** (Drysdale et al., 2001), that was not evident within this population except with respect to the laboratory grade. The laboratory course was very structured and test- oriented. These attributes align well with the tendencies for individuals with a dominant CS learning style. As Miller et al. (1993) noted in their study, “the students who had a cognitive ‘fit’ between task and setting and their personal style tended consistently to outperform their less ideally situated peers” (p. 34). In the laboratory situation, all of the learning style groups had A averages (see Table 4-2), but the CS students did have a significantly higher laboratory grade average. The decisive differences found in laboratory did not carry over into the student scores in the lecture exams.

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Many studies using measures of EF in pre-K and K children have found strong associations with subsequent **math** and reading **achievement** in elemen- tary school. The executive functions most consistently found to predict later academic success are attention, inhibition, and working memory. For example, those kindergarten children better able to wait as instructed before eating a piece of candy, or wait their turn when jointly building a tower, had better G1 **math** and reading outcomes, even when controlling for non-verbal intelligence and maternal education (von Suchodoletz, Trommsdorff, Heikamp, Wieber, & Goll- witzer, 2009). The effect size (0.62) was larger than the effect of non-verbal intel- ligence (0.18). Longitudinal studies that together include hundreds of 5 years old children demonstrate robust associations between tests of inhibition and mem- ory before entering kindergarten, and **math** and reading **achievement** in kinder- garten (Blair & Razza, 2007; Bull & Scerif, 2001; Welsh, Nix, Blair, Bierman, & Nelson, 2010; Willoughby, Magnus, Vernon-Feagans, Blair, & Family Life Project, 2017). Similar studies in kindergarten children predict **math** and reading **achievement** throughout elementary school and beyond (Al Otaiba & Fuchs, 2006; Best et al., 2011; Bull & Scerif, 2001; Monette, Bigras, & Guay, 2011; Van- denbroucke & Baeyens, 2017). In a meta-analysis of six longitudinal studies in- volving over 30,000 students, attention skills in kindergarten, along with early **math** and reading schools, predicted **math** and reading **achievement** throughout elementary and middle school (Duncan et al., 2007). Moreover, children who have trouble learning **math** have particularly poor attention focus (Swanson, 2006).

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Most of the participants in this study were underprivileged children who, due to social constraints, do not attain the **achievement** levels attainable under nor- mal conditions, nor even the average **achievement** levels of the country. They are normally deprived access to appropriate educational institutions and usually cannot benefit fully from those institutions they can access. They generally lack school preparation and are deficient in perception, speech, sorting, abstraction, reading and conceptual abilities. This lacuna gradually increases with time, often resulting in failure to graduate, thus perpetuating their inferior status beyond school into the realms of employment and personal life. The Ministry of Educa- tion’s inability to provide enrichment and cultivate knowledge for children from disadvantaged communities widens the gap between excellence and backward- ness and impairs the ability of weaker populations to advance, develop and cope with the modern world (Iram & Schmida, 1993).

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The reciprocal causation in the triadic system between environment and behavior represents the two-way influence between student’s **achievement** and their school environment. In the transactions of everyday life, behavior alters environmental conditions and is, in turn, altered by the very conditions it creates. The environment is not a fixed entity that inevitably impinges upon individuals. When mobility is constrained, some aspects of the physical and social environment may encroach on individuals whether they like it or not. But most aspects of the environment do not operate as an influence until they are activated by appropriate behavior. Lecturers do not influence students unless they attend their classes, teachers usually do not praise their students unless they do something praiseworthy. The aspect of the potential environment that becomes the actual environment for given individuals thus depends on how they behave. Because of the bi-directionality of influence between behavior and environmental circumstances, people are both products and producers of their environment. They affect the nature of their experienced environment through selection and creation of situations.

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