standardized test scores
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Author(s):  
Igor Himelfarb ◽  
Bruce L. Shotts ◽  
Andrew R. Gow

ABSTRACT Objective The main objective of this study was to evaluate the validity of grade point average (GPA) for predicting the National Board of Chiropractic Examiners (NBCE) Part I exam scores using chiropractic GPA. Methods Data were collected during the January 2019 computer-based testing administration of the NBCE's Part I exam. The sample size was n = 2278 of test takers from 18 domestic and 4 international chiropractic educational institutions. Six regression models were developed and tested to predict the Part I domain scores from chiropractic GPA while controlling for self-reported demographic variables. Residuals from the models were disaggregated by pre–chiropractic GPA. Results Chiropractic GPA revealed a positive, statistically significant correlation with sex. The chiropractic GPA was found to be a significant predictor of the Part I domain scores. A different perspective was obtained when residuals (observed minus predicted) were collected and split by the pre–chiropractic GPA. Very good students tended to be underpredicted, while other students were overpredicted. Conclusion This study builds on the cascading evidence from educational literature by providing additional results suggesting that undergraduate (prechiropractic) GPA as well as the GPA obtained in doctor of chiropractic programs are related to the future performance on the NBCE Part I exam. The results provide a first glance at the connection between the standardized test scores, which are often used for instructors' and institutional evaluation and the GPA obtained in a doctor of chiropractic program.


JAMA ◽  
2021 ◽  
Vol 326 (17) ◽  
pp. 1725
Author(s):  
Jakob Christensen ◽  
Betina B. Trabjerg ◽  
Yuelian Sun ◽  
Julie Werenberg Dreier

2021 ◽  
Author(s):  
Leonardo Bonilla-Mejía ◽  
Erika Londoño-Ortega

Rural schools are usually behind in terms of learning, and part of this could be related to geographical isolation. We explore this hypothesis, assessing the effect of distance between rural schools and local governments on learning in Colombia. We use spatial discontinuous regression models based on detailed administrative records from the education system and granular geographic information. Results indicate that distance to towns and Secretary of Education has significant negative effects on students’ standardized test scores. We evaluated alternative mechanisms, finding that the effect of distance is partly explained by differences in critical educational inputs, such as teachers’ education attainment and contract stability. Finally, we assess the mediating role of a program providing monetary incentives to teachers and principals in remote areas.


2021 ◽  
Author(s):  
Guilherme Lichand ◽  
Carlos Alberto Dória ◽  
Onício Leal Neto ◽  
João Cossi

Abstract Background: The transition to remote learning in the context of Covid-19 could lead to dramatic setbacks for school enrollment and learning outcomes, especially in developing countries – where a multiplicity of challenges, from limited connectivity to little support from parents, are bound to limit its effectiveness. To date, however, no study has rigorously documented the educational impacts of remote learning relative to in-person classes within primary and secondary education. Quantifying the extent of those losses, as well as the extent to which resuming in-person classes in the pandemic could at least partially offset them, is urgent, as governments worldwide struggle evaluating the trade-offs between the health and educational risks of reopening schools, with vaccination rates still dragging.Methods: Taking advantage of the fact that São Paulo featured in-person classes for the lion’s share of the first school quarter of 2020, but not thereafter, we estimate the effects of remote learning on secondary education, using a differences-in-differences strategy that contrasts variation in dropout risk and standardized test scores between the first and the last school quarters in 2020 to that in 2019, when all classes were in-person. We estimate heterogeneous effects by grade, student characteristics and school characteristics. We also estimate intention-to-treat (ITT) effects of reopening schools in the pandemic through a differences-in-differences strategy, contrasting differences between middle- and high-school students within municipalities that authorized in-person classes to partially return for the latter over the last quarter of 2020, to those within municipalities that did not.Findings: Dropout risk increased by 365% under remote learning. While risk increased with local disease activity, most of it can be attributed directly to the absence of in-person classes:we estimate that dropout risk increased by no less than 247% across the State, even at the low end of the distribution of per capita Covid-19 cases. Average standardized test scores decreased by 0.32 standard deviation, as if students had only learned 27.5% of the in-person equivalent under remote learning. Learning losses did not systematically increase with local disease activity, attesting that they are in fact the outcome of remote learning, rather than a consequence of other health or economic impacts of Covid-19. Authorizing schools to partially reopen for in-person classes increased high-school students’ test scores by 20% relative to the control group.Interpretation: Results show that the societal costs of keeping schools closed in the pandemic are very large. While the learning losses that we document are at least as large as those documented in developed countries on the aftermath of the first Covid-19 wave, the dramatic surge in dropout risk is unique to developing countries. Such massive impacts are likely to bring about long-lasting effects on employment, productivity, and poverty levels. Our findings highlight that reopening schools under safe protocols can prevent those costs from growing even larger. They also caution against recent enthusiasm for remote learning in primary and secondary education outside the context of Covid-19.Funding: Research funded by the Inter-American Development Bank (IADB) as part of a partnership between IADB and the São Paulo State Education Secretariat.


2021 ◽  
Vol 12 ◽  
Author(s):  
Andrea Westphal ◽  
Miriam Vock ◽  
Julia Kretschmann

The Big Five personality traits play a major role in student achievement. As such, there is consistent evidence that students that are more conscientious receive better teacher-assigned grades in secondary school. However, research often does not support the claim that students that are more conscientious similarly achieve higher scores in domain-specific standardized achievement tests. Based on the Invest-and-Accrue Model, we argue that conscientiousness explains to some extent why certain students receive better grades despite similar academic accomplishments (i.e., achieving similar scores in domain-specific standardized achievement tests). Therefore, the present study examines to what extent the relationship between student personality and teacher-assigned grades consists of direct as opposed to indirect associations (via subject-specific standardized test scores). We used a representative sample of 14,710 ninth-grade students to estimate these direct and indirect pathways in mathematics and German. Structural equation models showed that test scores explained between 8 and 11% of the variance in teacher-assigned grades in mathematics and German. The Big Five personality traits in students additionally explained between 8 and 10% of the variance in grades. Finally, the personality-grade relationship consisted of direct (0.02 | β| ≤ 0.27) and indirect associations via test scores (0.01 | β| ≤ 0.07). Conscientiousness explained discrepancies between teacher-assigned grades and students’ scores in domain-specific standardized tests to a greater extent than any of the other Big Five personality traits. Our findings suggest that students that are more conscientious may invest more effort to accomplish classroom goals, but fall short of mastery.


2021 ◽  
pp. 1-24
Author(s):  
Avidit Acharya ◽  
Kirk Bansak ◽  
Jens Hainmueller

Abstract We introduce a constrained priority mechanism that combines outcome-based matching from machine learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold $\bar g$ for the minimum acceptable average outcome score that should be achieved by the assignment. In the refugee matching context, this score corresponds to the probability of employment, whereas in the student assignment context, it corresponds to standardized test scores. The mechanism is a priority mechanism that considers both outcomes and preferences by assigning agents (refugee families and students) based on their preferences, but subject to meeting the planner’s specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the planner’s threshold.


Author(s):  
Tiffany Williams

The main objective of this study was to understand how teacher leadership and teacher quality impacted fourth grade student performance on the Louisiana Education Assessment Program (LEAP). The participants chosen were six schools in Louisiana who were labeled academically unacceptable for at least three years that taught fourth grade. A review of the data results provide an indication that, although standardized test scores of students are one piece of information for school leaders to use to make judgments about teacher effectiveness, such scores should be only a part of an overall comprehensive evaluation of the role of teacher leadership and teacher quality on student performance. The results varied by the characteristic of teacher quality and its impact on student performance.


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