Reflection 27: Acuity as an English II Predictor Methods Linear regression allows researchers to analyze cause and effect or predictive relationships among variables (Creighton, 2007). For this assignment, I set out to conduct a regression analysis in hopes of answering two questions: (1) is there a relationship between student scores on Acuity, our school-wide interim testing program, and their performance on the English II state assessment?, and (2) if the relationship is significant, can Acuity results be used to predict student performance on the state test? To conduct this analysis I used the 2015 Partnership for Assessment of Readiness for College and Careers (PARCC) English II assessment results as well as the Acuity data from the first semester screener which was given in December 2014. This test was the last comprehensive Acuity exam given to English II students during the 2014-2015 school year (a PARCC-issued practice test was used during third nine-weeks exams). My student sample included all students who had both a December Acuity score in the Acuity online system and a Spring 2015 PARCC score in School Status, the school’s database for teacher and student information. With these requirements, the total sample size included 64 students. To run my statistical analysis, I created a Google Sheet document with three columns including the student name (1), their percent correct on the December 2014 Acuity Diagnostic test (2), and their scale score for the Spring
Student achievement is one of the driving factors in education and, quite possibly, the most important. Educators strive to help students improve achievement through quality instructional practices and safe and effective learning environments, but this does not always correlate to adequate performance on standardized testing used to evaluate college or career readiness. One of the measures utilized to evaluate student achievement is the ACT test. Historically, the ACT has provided a measure of college readiness and one that became very serious for Kentucky schools as it is now part of the state accountability
Analysis of the data yielded a correlation of 0.51 and variance of 0.26. In other words, 26% of the variance in the Keystone exam scaled score is predicted by the CDT scaled score. When used to predict future performance on the Keystone exam, the use of the t statistic would refute the existence of a connection between the CDT score and that of the Keystone score. An assumption that the predicted means for the 2014 – 2015 Keystone score and the actual Keystone score produced a P value less than 0.001. This is far smaller than the required 0.05 and requires us to accept the alternate hypothesis, that the means are not connected. The final comparative component demonstrated that the CDT accurately predicted student success or lack there of in 57% of all cases. In the remaining 43% of erroneous predictions, the CDT predicted that students would not succeed when in fact, they did 70% of the time. In other words, for 13% of all students, the CDT erroneously predicted student success in Operations with Real Numbers and Expressions when they in fact did not meet
For many years, standardized tests have been a pillar of college admissions. Students are persuaded to take the Scholastic Assessment Test (SAT) or the American College Test (ACT) because colleges believe the scores can predict an applicant’s academic success after high school. However, an increasing number of colleges have made reporting test scores optional due to inconsistencies with the tests, many of which have been emphasized by students. These inconsistencies and other problems with test distribution have led to increasing demands for standardized testing to be reformed or become optional in the admissions process. Standardized testing should be eliminated as a criterion for college applicants because the tests have made education less significant, have made scores vary among students with similar academic abilities, and have not contributed a noticeable improvement to children’s intelligence.
6. Use IBM® SPSS® software to compute all the descriptive statistics for the following set of three test scores over the course of a semester. Which test had the highest average score? Which test had the smallest amount of variability?
The College Board and ACT nonprofit organizations, known for developing and administering the Scholastic Aptitude Test (SAT) and American College Testing (ACT) assessment respectively, represents higher education’s widely accepted college readiness determinant for prospective students. These examinations empirically measure a student’s grasp of reading, writing, and mathematics – subjects taught every day in high school classrooms. As a result, they typically constitute a significant proportion of the total entrance requirements for prospective students to relevant institutions of higher learning and denote a serious endeavor unto itself. Students commonly take one or both of these examinations during their junior or senior year of high school as dictated by an institution’s administrative guidelines, although most colleges now allow either test as part of their proprietary admission formulas. And since it turns out there exists subtle differences in the tests themselves, students should review research concluding certain individuals may be better candidates for maximizing performance on one examination versus another.
Since this study is strictly quantitative, it faces the limitations of not being suited to answer how and why questions. The data analyzed may not be robust enough to explain complex issues, and makes it difficult to understand the context of a phenomenon (Mills and Gay, 2016). The data in this study indicate the number of opportunities at a given school, but gives little information of the quality about these opportunities. For example, multiple schools may offer Advanced Placement (AP) courses, but the quality of the instruction and implementation of these courses could vary significantly across schools. Additionally, the data do not take into account local school policies and practices that differ between cities, districts, and schools. Moreover, accessing secondary data may be difficult – although the data are readily available online on the school level, it may not be possible to gather all the data into one comprehensive file. Also, since the datasets vary in the types of schools included (e.g. some datasets may exclude some charter schools), the process of merging the files may not result in a one hundred
Standardized tests do not accurately measure a student’s intelligence or growth. There are multiple factors that could hinder a student’s performance on tests. First of which being test anxiety, not all students test well or perform well in high-intensity situations. Personal issues also play a large role in how well a student is able to perform on their test; if a student’s attention is elsewhere they are more likely to be less focused. Standardized tests also do not focus on
After reading chapter 5 from Reign Error I learned that the National Assessment of Educational Progress (NAEP) is a terrible way to measure students academic performance because people don’t understand the scores. NAEP is a way of determining if American students are doing well or badly, it is administered to samples of students; no one knows who will take it and no one can prepare for it. The results of these scores confuse the public to what expectations are appropriate for the students. NAEP grade students using advanced, proficient, and basic, but these grades don’t represent students grades. The NAEP test score show that students have improved in reading and mathematics over the last 20 years. The book shows some graphs of fourth graders
Standardized tests can be found at any level of a student’s academic career, but are they accurate indicators of a student’s academic abilities? Standardized tests are used to measure a student 's academic abilities, and overall knowledge. In theory, a student 's skills can be determined by examining the limited data collected from the test. However, standardized test do not fully represent a student 's abilities, and cumulative knowledge. Many factors may affect the validity of the scores, and the accuracy of the assessment. Instructor’s teaching directly for the test, being able to guess on multiple choice questions, examining only test scores, and ignoring other academic factors contribute to the biased representation of students’ academic abilities.
The next alternative recognizes the fact that a “one-size-fits-all” approach to standardized testing does not reflect the abilities of all demographics taking the test (NACAC, “Standardized Tests In Admission”). Despite this fact, the National Association for College Admission Counseling believes that to some degree, “Standardized tests are important predictors of students’ academic success” (NACAC, “Standardized Tests In Admission”). While the Commission supports the use of standardized tests, they believe that all students should be provided with the necessary preparation before taking the test.
The primary research question guiding this study is: What is the relationship between state- level high-stakes testing pressure and student achievement? More specifically, we want to know: What is the pattern of correlations between APR and fourth and eighth grade NAEP scores in reading and math over time, when disaggregated by student ethnicity, and when disaggregated by student socioeconomic status. (p.
Thesis: Standardized tests such as the ACT and SAT are not the most accurate way to measure a students aptitude and intelligence, therefore schools should pay more attention to graduation and dropout rates, enrollment into advanced placement classes, as well as extracurricular activities.
No single test can fully and accurately measure student achievement. They should not be used to make major education decisions about students or their schools. Decisions such as these should be based on relevant information like grades and teacher recommendations. Measuring achievement and success only or mostly by SOL scores is inaccurate. The state’s own validity and reliability
There are a variety of topics that are interesting in life. This interest may then become a point of inquisition, where an individual may formulate a relationship between two variables, which may or may not influence each other. Next, a hypothesis is formed and tested. In this same manner, a school educator was interested in determining the potential relationship between grade point average (GPA) and IQ scores among ninth graders. The educator random sampled 30 ninth graders, ages 14 years old and administered the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV). This writer will be expanding further on this topic and will formulate the null and alternative hypothesis, describe the four scales of measurement, describe whether if there is a correlation significant (positive, negative, or no correlation) enough between both variables, describe the strength of the relationship, describe what the results reveals about the hypothesis, and what conclusions can be drawn from the results.
Kurtis’ overall achievement in reading and written expression fell within the average range with slightly low average scores in reading fluency and oral reading when compared to his same aged peers. Kurtis struggled with word attack skills and had difficulty with sounding out of words. Kurtis could identify beginning sounds, but when he was asked to read nonsense words he struggled with short vowel sounds and correct pronunciation. However, Kurtis’ Letter-Word Identification and Passage Comprehension were within the average range. When he read sentences orally he mispronounced words, and did not slow down to correct his errors even when they did not make sense. On the reading fluency subtest, he was required to read a short sentence and