scholarly journals Linking TIMSS and NAEP assessments to evaluate international trends in achievement

2017 ◽  
Vol 25 ◽  
pp. 11 ◽  
Author(s):  
Hwanggyu Lim ◽  
Stephen G. Sireci
Keyword(s):  
No Child Left Behind ◽  
Mathematics Achievement ◽  
Eighth Grade ◽  
Advanced Mathematics ◽  
Achievement Level ◽  
Left Behind ◽  
International Trends ◽  
The Us ◽  
Eighth Grade Students ◽  
Mathematics And Science

The Trends in International Mathematics and Science Study (TIMSS) makes it possible to compare the performance of students in the US in Mathematics and Science to the performance of students in other countries. TIMSS uses four international benchmarks for describing student achievement: Low, Intermediate, High, and Advanced. In this study, we linked the eighth-grade Math TIMSS and NAEP scales using equipercentile equating to (a) help better interpret U.S. eighth-grade students’ performance on TIMSS, and (b) investigate the progress of eighth-grade U.S. students over time relative to the progress of students in other countries. Results indicated that relative to other countries, U.S. eighth-grade students increased with respect to the “At or Above Basic” NAEP Achievement level, but that other countries saw larger improvements in the higher achievement level categories, relative to the US. This finding may reflect the emphasis of No Child Left Behind on raising lower achievement to “proficient.” However, with respect to “Advanced” mathematics achievement, eighth-grade U.S. students showed less improvement than students in other countries. 

Has NCLB Encouraged Educational Triage? Accountability and the Distribution of Achievement Gains

2017 ◽  
Vol 12 (1) ◽  
pp. 77-106 ◽  
Author(s):  
Dale Ballou ◽  
Matthew G. Springer
Keyword(s):  
No Child Left Behind ◽  
Mathematics Achievement ◽  
Adequate Yearly Progress ◽  
High Stakes ◽  
Achievement Gains ◽  
Left Behind ◽  
Before And After ◽  
Closing The Gap ◽  
Reasonable Prospect ◽  
Identification Strategy

The No Child Left Behind Act (NCLB) has been criticized for encouraging schools to neglect students whose performance exceeds the proficiency threshold or lies so far below it that there is no reasonable prospect of closing the gap during the current year. We examine this hypothesis using longitudinal data from 2002–03 through 2005–06. Our identification strategy relies on the fact that as NCLB was phased in, states had some latitude in designating which grades were to count for purposes of a school making adequate yearly progress. We compare the mathematics achievement distribution in a grade before and after it became a high-stakes grade. We find in general no evidence that gains were concentrated on students near the proficiency standard at the expense of students scoring much lower, though there are inconsistent signs of a trade-off with students at the upper end of the distribution.

scholarly journals FINDING THE BEST ALGORITHMS AND EFFECTIVE FACTORS IN CLASSIFICATION OF TURKISH SCIENCE STUDENT SUCCESS

2019 ◽  
Vol 18 (2) ◽  
pp. 239-253 ◽  
Author(s):  
Enes Filiz ◽  
Ersoy Öz
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Eighth Grade ◽  
Support Vector ◽  
Educational Strategies ◽  
Vector Machines ◽  
Eighth Grade Students ◽  
Timss 2015 ◽  
Mathematics And Science

Educational Data Mining (EDM) is an important tool in the field of classification of educational data that helps researchers and education planners analyse and model available educational data for specific needs such as developing educational strategies. Trends International Mathematics and Science Study (TIMSS) which is a notable study in educational area was used in this research. EDM methodology was applied to the results of TIMSS 2015 that presents data culled from eighth grade students from Turkey. The main purposes are to find the algorithms that are most appropriate for classifying the successes of students, especially in science subjects, and ascertaining the factors that lead to this success. It was found that logistic regression and support vector machines – poly kernel are the most suitable algorithms. A diverse set of features obtained by feature selection methods are “Computer Tablet Shared”, “Extra Lessons Last 12 Month”, “Extra Lessons How Many Month”, “How Far in Education Do You Expect to Go”, “Home Educational Resources”, and “Student Confident in Science” and these features are the most effective features in science success. Keywords: classification algorithms, educational data mining, eighth grade, science success, TIMSS 2015.

The development of linguistic complexity: A functional continuum

2010 ◽  
Vol 45 (3) ◽  
pp. 329-346 ◽  
Author(s):  
Elizabeth O'Dowd
Keyword(s):  
No Child Left Behind ◽  
New England ◽  
Language Proficiency ◽  
English Learners ◽  
Meaning Making ◽  
Linguistic Complexity ◽  
Systematic Analysis ◽  
Left Behind ◽  
The Us ◽  
K 12

Following the No Child Left Behind Act (2001), which mandates standards-based accountability for the academic progress of all students, much attention has been given to integrating language and content instruction for English learners (ELs) in K-12 classrooms in the US. Although TESOL and other state-approved language proficiency standards acknowledge that academic English requires progressive linguistic complexity to tackle progressively complex content, they give no indicators for this progression beyond some generalizations about increased sentential variety. An enlightening characterization of linguistic complexity comes from Systemic Functional Linguistics (SFL), specifying how grammatical choices actually construct meaning, making a strong case for explicit, proactive instruction, and calling for a systematic analysis of the language our English learners need to master. This paper describes an ongoing project to answer this call by charting a developmental continuum of complexity for school-age English learners. Its preliminary analysis is based on some 90 compositions, collected over the course of a year from more than 30 students in a New England middle school classroom.

Eight Voices of Empowerment

2016 ◽  
Vol 52 (4) ◽  
pp. 525-552 ◽  
Author(s):  
Brian R. Horn
Keyword(s):  
Middle School ◽  
No Child Left Behind ◽  
Action Research ◽  
Language Arts ◽  
Title I ◽  
Eighth Grade ◽  
Student Empowerment ◽  
Action Research Project ◽  
Research Project ◽  
Left Behind

This article explores student empowerment in a restructured urban Title I middle school. The study includes data from eight participants in an action research project that involved a critical inquiry unit in an eighth-grade language arts class that asked students, “How are you empowered and disempowered by school?” Findings reveal that although No Child Left Behind (NCLB) policies are said to empower students, student empowerment is rather a negotiated process that requires teachers to help students develop an eye for fairness, create opportunities for them to express themselves in new ways, and encourage them to learn from one another.

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