scholarly journals Educational production and its determinants in Paraguay according to PISA-D 2018

2021 ◽  
Vol 27 (53) ◽  
pp. 55-67
Author(s):  
Julio Ramón Pacher
Keyword(s):  
Educational Production

scholarly journals The Social Issues Pedagogy vs. The Traditional Principles of Economics: An Empirical Examination

1998 ◽  
Vol 42 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Paul W. Grimes ◽  
Paul S. Nelson
Keyword(s):  
Production Function ◽  
Standardized Test ◽  
Social Issues ◽  
Student Attrition ◽  
Empirical Examination ◽  
Educational Production Function ◽  
The Social ◽  
Educational Production ◽  
Economics Courses ◽  
Prior Experiences

Standardized test (TUCE) scores for students enrolled in a Social Issues course were compared to those of students in traditional Principles of Economics courses within the framework of a standard educational production function. The production function was estimated using Heckman's two-step procedure to correct for self-selection due to student attrition over the course of study. After controlling for student demographics, prior experiences, and academic aptitude, no significant test score differences were found between students in the Social Issues course and those in the Principles of Macroeconomics. However, Social Issues students were found to score significantly below students in the Principles of Microeconomics, ceteris paribus. The results also indicate that students had a higher probability of completion in the Social Issues course relative to a theory oriented Principles course.

Revisiting the Relationship Between International Assessment Outcomes and Educational Production

2016 ◽  
Vol 53 (4) ◽  
pp. 1054-1085 ◽  
Author(s):  
Martin Carnoy ◽  
Tatiana Khavenson ◽  
Prashant Loyalka ◽  
William H. Schmidt ◽  
Andrey Zakharov
Keyword(s):  
Educational Production ◽  
International Assessment ◽  
Assessment Outcomes ◽  
The Relationship

scholarly journals Mobile Technology as a Learning Tool in the Academic Environment

2019 ◽  
Vol 11 (2(J)) ◽  
pp. 92-102
Author(s):  
Sidwell Sabelo Nkosi ◽  
Rosemary Sibanda ◽  
Ankit Katrodia
Keyword(s):  
Mobile Technology ◽  
Low Income ◽  
Ordinary Least Squares ◽  
Cumulative Distribution ◽  
Educational Institutions ◽  
Learning Tool ◽  
Academic Activities ◽  
Kwazulu Natal ◽  
Educational Production ◽  
The University

Education in South Africa is not equally accessible, and the quality of education is not the same across all educational institutions. Students from low-income societies are scoring lower marks in contrast to students from higher income societies. The influence on this is the unavailability of efficient educational resources and infrastructure. This study uses a focus group of 300 students from the University of KwaZulu-Natal (UKZN) School of Economics. It attempts to examine and explain the effect of the use of mobile technology in academic activities within the school of economics at UKZN. The study divides the sample size into two groups, half is given mobile technology and the remaining group is deprived of mobile technology. The data is recorded in two educational production functions, namely Ordinary Least Squares and Logistic Regression Model. The cumulative distribution function examines the probability, in form of Logit, that a student passes economic if using mobile technology for academic activities or studying. Study findings indicate that it is imperative that institutions invest in mobile technology as their learning tool to improve throughput rate and it allows efficiency in all academic activities. Mobile technology enables students to be disciplined, effective and work ready.

scholarly journals Exploring the Communist Higher Education System. Contributions towards a Theoretical Framework

2014 ◽  
Vol 4 (2) ◽  
pp. 5-24
Author(s):  
Bogdan Florian
Keyword(s):  
Higher Education ◽  
Education Reform ◽  
Social Order ◽  
Industrial Development ◽  
Theoretical Framework ◽  
Higher Education Reform ◽  
Planning System ◽  
Communist Manifesto ◽  
Communist System ◽  
Educational Production

Abstract: ‘The combination of education with industrial production’ made the top 10 list of measures which have to be implemented in countries where the proletariat will raise as the ruling class, according to Marx’s Communist Manifesto. It is ranked at the end of the list of necessary steps to achieve a new social order, however it endured and, in a slightly modified form it even exists today in higher education reform strategies. To which extent has this ideological prescription been followed and inspired political measures? This paper aims at proposing a few steps in creating a theoretical framework for analysis of the role of higher education in the communist system. I will use an institutionalist approach to explore where higher education can be placed, in the larger context of the communist system. I will try and adapt to this topic the system paradigm proposed by János Kornai and explore higher education as a component of the larger communist system. Was it the universities’ mission to produce an able workforce for the industrial development? Was there another scientific or ideological mission equally important? How well did the communist central planning system perform in matching industrial demand and educational production? These are some key questions to which this exploration aims at finding a framework for answering.

Educational Production Functions

1978 ◽  
Vol 3 (3) ◽  
pp. 209-231 ◽  
Author(s):  
Solomon W. Polachek ◽  
Thomas J. Kniesner ◽  
Henrick J. Harwood
Keyword(s):  
Academic Achievement ◽  
Maximum Likelihood ◽  
Production Function ◽  
Production Functions ◽  
Elasticity Of Substitution ◽  
Likelihood Methods ◽  
Educational Backgrounds ◽  
Scholastic Performance ◽  
Maximum Likelihood Methods ◽  
Educational Production

This research examines scholastic performance within the context of an individual’s production function. A constant partial elasticity of substitution production function for academic achievement is presented and estimated with non linear maximum likelihood methods. We find that ability and time devoted to various aspects of the learning process are the most important determinants of students’ accomplishments. Our results underscore the potential for students to compensate for relatively “poor” educational backgrounds by spending more time on study and class attendance.

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