nep-edu New Economics Papers
on Education
Issue of 2019‒04‒08
three papers chosen by
Marco Novarese
Università del Piemonte Orientale

  1. The impact of a mathematics computer-assisted learning platform on students' mathematics test scores By Perera, Marcelo; Aboal, Diego
  2. How skills and parental valuation of education influence human capital acquisition and early labor market return to human capital in Canada By Steven Lehrer
  3. Selection and educational attainment: Why some children are left behind? Evidence from a middle-income country By Luciana Méndez-Errico; Xavier Ramos

  1. By: Perera, Marcelo (CINVE, and Facultad de Ciencias Económicas y Administración, Universidad de la República de Uruguay); Aboal, Diego (Facultad de Ciencias Económicas y Administración, Universidad de la República de Uruguay, and Universidad ORT Uruguay)
    Abstract: Since 2013, the Uruguayan educational system has been using an online adaptive learning tool for mathematics: The Mathematics Adaptive Platform (PAM for its Spanish acronym). PAM's content has been adapted to the national curriculum and it is a tool that - based on an analysis of students' experiences - offers personalised feedback according to each student's skill level. The use of PAM has been spreading throughout the education system. By 2016, approximately half of all students in 3rd through 6th grades of primary education had used the platform. The purpose of this study is to identify the effect of the use of PAM on the test score gain in mathematics based on longitudinal data from a sample of students in primary education. The results show a positive effect of 0.2 standard deviations on mathematics test scores. Results also show that the impact of PAM increases as the socioeconomic status of students decreases. There is no heterogeneous impact by gender. This is the first evidence at a country-wide level of the impact of a pedagogical tool of this type.
    Keywords: Evaluation of Computer Assisted Learning systems, Mathematics, Uruguay
    JEL: I21 I28
    Date: 2019–03–12
    URL: http://d.repec.org/n?u=RePEc:unm:unumer:2019007&r=all
  2. By: Steven Lehrer
    Abstract: Using the Youth in Transition Survey we estimate a Roy model with a three dimensional latent factor structure to consider how parental valuation of education, cognitive skills and non-cognitive skills influence endogenous schooling decisions and subsequent labour market outcomes in Canada. We ï¬ nd the effect of cognitive skills on adult incomes arises by increasing the likelihood of obtaining further education. Further, we ï¬ nd that both non-cognitive skills and parental valuation for education play a larger role in determining income at age 25 than cognitive skills. Last, our analysis uncovers striking differences between men and women in several of the estimated relationships. Speciï¬ cally, simulations of the estimated model illustrate that i) among the low skilled, women have much higher college graduation rates, ii) the age 25 earnings gradient by either skill measure is much flatter for women, and iii) parental valuation of education plays a larger role in influencing young women than men.
    Keywords: skills , schooling decisions, early labour market outcomes, gender differences, parental valuation of education
    JEL: J24 C38
    Date: 2019–03
    URL: http://d.repec.org/n?u=RePEc:qed:wpaper:1416&r=all
  3. By: Luciana Méndez-Errico (Universidad de la República); Xavier Ramos (Universitat Autònoma de Barcelona)
    Abstract: We model schooling as a sequential process and examine why some children are left behind. We focus on the factors that explain selection at early stages of the education system. Our findings for Uruguay suggest that long-term factors, such as parental background or ethnicity matter across all education stages while the effect of short-term factors, such as family income, wear out as individuals progress in the education system, suggesting a severe selection process at early stages.
    Keywords: Schooling transition, selection, inequality, education, ethnicity, cognitive and non-cognitive abilities, sequential dynamic model.
    JEL: I20 I24 J13 J15 J24
    Date: 2019–01
    URL: http://d.repec.org/n?u=RePEc:inq:inqwps:ecineq2019-487&r=all

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