scholarly journals Teacher and Student Practices Associated with Performance in the PISA Reading Literacy Evaluation

2021 ◽  
Vol 6 ◽  
Author(s):  
Luis Rojas-Torres ◽  
Graciela Ordóñez ◽  
Karen Calvo
Keyword(s):  
Costa Rica ◽  
Linear Regression ◽  
Teacher Practices ◽  
Contextual Factors ◽  
Economic Resources ◽  
Reading Literacy ◽  
Sampling Weights ◽  
Reading Score ◽  
Plausible Values ◽  
Student’S Performance

This article aims at finding teacher’s and student’s practices that relate to performance in PISA reading literacy evaluations and that are feasible to intervene in order to assist the improvement of reading competency. To achieve this purpose, the study was developed with data collected from the population of Costa Rica that took the PISA evaluation in 2018 (n = 4691, 2340 men, and 2351 women). A linear regression of the reading score was performed utilizing plausible values and sampling weights. The predictors of the regression were contextual factors, teacher practices, and student habits. Time spent and interest in reading showed a positive and relevant association with student’s performance in reading, controlling important background aspects like economic resources and parents' education. Moreover, 28.19% to the obtained variance explanation of the reading literacy (27%) was only due to the teacher’s and student’s practices. These results provide favorable information to design interventions for the improvement of reading competency.

scholarly journals Exam cheating among Quebec’s preservice teachers: the influencing factors

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Sylvie Fontaine ◽  
Eric Frenette ◽  
Marie-Hélène Hébert
Keyword(s):  
Preservice Teachers ◽  
Linear Regression ◽  
Influencing Factors ◽  
Online Survey ◽  
Contextual Factors ◽  
Institutional Context ◽  
Regression Analyses ◽  
Factors Associated ◽  
Student Cheating ◽  
Faculties Of Education

AbstractThis article presents the results of a research that aimed to examine the phenomenon of student cheating on exams in faculties of education in Quebec universities. A total of 573 preservice teachers completed an online survey in 2018. The questionnaire consisted of 28 questions with a Likert scale related to individual and contextual factors associated with the propensity to cheat on exams as well as two yes/no items on the arguments for cheating. Descriptive and hierarchical linear regression analyses highlighted the existence of cheating but also how three factors influenced the students’ propensity to cheat: influence of peers, methods of cheating, and institutional context.

scholarly journals PENGARUH KEMAMPUAN LITERASI MEMBACA TERHADAP PRESTASI BELAJAR SISWA SEKOLAH DASAR

2021 ◽  
Vol 13 (1) ◽  
pp. 52-58
Author(s):  
Saeful Amri ◽  
Eliya Rochmah
Keyword(s):  
Student Achievement ◽  
Data Analysis ◽  
Linear Regression ◽  
Survey Research ◽  
Significant Influence ◽  
Reading Skills ◽  
Literacy Skills ◽  
Reading Literacy ◽  
Simple Linear Regression ◽  
Regression Test

The background of this research is the low of reading literacy skills in SDN Pegagan. The low of reading skills is thought to have an effect on student achievement as well. This is indicated by the results of student achievement that are still low. The purposes of this research are to (1) Know the reading literacy skills of student (2) Know the level of student achievement, and (3) Know the influence of reading literacy skills on student achievement in SDN Pegagan. The study design used quantitative survey research with a sample of 88 students in SDN 1, 2, and 3 Pegagan. Data analysis used simple linear regression test. The results showed that (1) the reading literacy skills of students was quite sufficient at 67.53%, (2) The students' learning achievement was classified as very good at 81%, and (3) There was a significant influence of 5.4% between reading literacy skills and student achievement learning.

scholarly journals Estimating a Multilevel Model with Complex Survey Data: Demonstration using TIMSS

2020 ◽  
Vol 18 (2) ◽  
pp. 2-20
Author(s):  
Julie Lorah
Keyword(s):  
Survey Data ◽  
Multilevel Model ◽  
Model Description ◽  
Sampling Weights ◽  
Complex Survey ◽  
Complex Survey Data ◽  
Plausible Values

Analysis of complex survey data is demonstrated for the multilevel model. Description of specific aspects of analysis, including plausible values, sampling weights, and replicate weights is provided. Following this, example TIMSS data and models are described and results are presented.

scholarly journals PENGARUH TINGKAT LITERASI BACA SISWA TERHADAP EFEKTIVITAS PEMBELAJARAN

2022 ◽  
Vol 6 (1) ◽  
Author(s):  
Yudho Ramafrizal S ◽  
Teni Julia Somadi
Keyword(s):  
Linear Regression ◽  
Descriptive Analysis ◽  
Linear Regression Analysis ◽  
Reading Literacy ◽  
Learning Effectiveness ◽  
Simple Linear Regression ◽  
Average Value ◽  
Student Reading ◽  
Literacy Level ◽  
Survey Research Methods

The title of this study is the Effect of Student Reading Literacy Level on Learning Effectiveness (Survey on Introduction of Accounting Class X Accounting at SMK Negeri 3 bandung academic year 2021-2022). This research aims to (i) find out the level of student read literacy in Introduction of Accounting of class X Accounting at SMK Negeri 3 bandung (ii) to know the level of learning effectiveness Introduction of Accounting of class X Accounting at SMK Negeri 3 bandung (iii) knowing the magnitude of the influence of students' reading literacy levels on the effectiveness of learning in Introduction of Accounting of class X Accounting at SMK Negeri 3 bandung. In this study the authors used quantitative approach methods using survey research methods, with a population of 30. To seek influence, The data analysis used is a simple linear regression analysis using IBM SPSS Statistics Version 24.0,descriptive analysis ofreading literacy level with a weight of 37.97 and an average value of 3.80, and theeffectiveness of learning with a sum weight of 40.03 and an average value of 4.00, on a simple linear regression test there is a regression coefficient value obtained is 0.580, and the determination coefficient valueis obtained the number R (correlation coefficient) or the relationship number between variable X to variable Y of 0.557 or 55.7%. The amount of contribution made by the variable level of reading literacy can be seen from the number in R Square which is 0.310 which means that the level of reading literacy affects the effectiveness of learning by 31.0%. While the other 69.0% was influenced by other factors outside of the research conducted.

scholarly journals An Exploration of Impact Factors Influencing Students’ Reading Literacy in Singapore with Machine Learning Approaches

2019 ◽  
Vol 9 (5) ◽  
pp. 52 ◽  
Author(s):  
Xin Dong ◽  
Jie Hu
Keyword(s):  
Machine Learning ◽  
Contextual Factors ◽  
School Level ◽  
Reading Literacy ◽  
Support Vector ◽  
Learning Approach ◽  
Accuracy Score ◽  
International Student Assessment ◽  
Machine Learning Approach ◽  
The Impact

This study identified the contextual factors which differentiated 15-year-old students with high- and low-achieving reading literacy in Singapore based on Program for International Student Assessment (PISA) 2015. 4,015 students from Singapore were collected from the public dataset of PISA 2015, with 2,646 high-achieving students and 1,369 low-achieving students in PISA reading literacy test. The impact of the overall 49 contextual factors on reading literacy was analyzed in three levels: student level, family level and school level. Support vector machine (SVM), a machine learning approach, was applied to analyze these contextual features. It indicated that SVM could effectively distinguish these two cohorts of readers with an accuracy score of 0.78. SVM-based recursive feature elimination (SVM-RFE), another machine learning approach, was then applied to rank these selected features. These features were outputted in descending order with regard to the degree of their significance to the differentiation. At last, an optimal set with 15 contextual factors was selected by RFE-CV (cross validation), which collectively affected the differentiation of students with high- and low-level of reading literacy. Based on the analysis, implications to further improving students’ reading literacy can be achieved.

scholarly journals Social contextual factors contributing to child and adolescent labor: an ecological analysis

2011 ◽  
Vol 45 (4) ◽  
pp. 676-685 ◽  
Author(s):  
Vilma Sousa Santana ◽  
Martha Suely Itaparica
Keyword(s):  
Social Support ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Cumulative Incidence ◽  
Public Services ◽  
School Quality ◽  
Social Deprivation ◽  
Contextual Factors ◽  
Child And Adolescent ◽  
Poor School

OBJECTIVE: To examine the relationship between social contextual factors and child and adolescent labor. METHODS: Population-based cohort study carried out with 2,512 families living in 23 subareas of a large urban city in Brazil from 2000 to 2002. A random one-stage cluster sampling was used to select families. Data were obtained through individual household interviews using questionnaires. The annual cumulative incidence of child and adolescent labor was estimated for each district. New child and adolescent labor cases were those who had their first job over the two-year follow-up. The annual cumulative incidence of child and adolescent labor was the response variable and predictors were contextual factors such as lack of social support, social deprivation, unstructured family, perceived violence, poor school quality, poor environment conditions, and poor public services. Pearson's correlation and multiple linear regression were used to assess the associations. RESULTS: There were selected 943 families corresponding to 1,326 non-working children and adolescents aged 8 to 17 years. Lack of social support, social deprivation, perceived violence were all positively and individually associated with the annual cumulative incidence of child and adolescent labor. In the multiple linear regression model, however, only lack of social support and perceived violence in the neighborhood were positively associated to child and adolescent labor. No effect was found for poor school quality, poor environment conditions, poor public services or unstructured family. CONCLUSIONS: Poverty reduction programs can reduce the contextual factors associated with child and adolescent labor. Violence reduction programs and strengthening social support at the community level may contribute to reduce CAL.

Debt and Wrong-Way Resource Flows in Costa Rica

1990 ◽  
Vol 4 ◽  
pp. 107-121 ◽  
Author(s):  
Sheldon Annis
Keyword(s):  
Costa Rica ◽  
Natural Resources ◽  
Physical Environment ◽  
Costa Rican ◽  
Economic Resources ◽  
Ethical Aspect ◽  
Resource Flows ◽  
The Social ◽  
The Right ◽  
The Third World

External debt, poverty, and the use of natural resources are inextricably linked. This article examines an ethical aspect of that linkage: the social direction of resource flow. It argues that the direction in which a country's economic resources are transferred—from poor to rich, or rich to poor—also sets the pattern for the flow of natural resources. By extension, the same kinds of forces that tend to impoverish human environments also tend to impoverish the physical environment; and conversely, that which tends to restore or promote equity generally tends to be good for the environment. For the past forty years, Costa Rican government policies have been among the fairest and most environmentally progressive in the Third World; yet Costa Rica is heavily in debt in both the economic and environmental sense. Are the “right” policies not right–or are they morally right but not workable? Annis examines this paradoxical question using the notion of “dual debt” and “wrong-way resource flows.”

scholarly journals A Multilevel Study Based on PISA 2003: School Effectiveness Factors in the area of Mathematics

2006 ◽  
Vol 14 ◽  
pp. 29
Author(s):  
Covadonga Ruiz de Miguel ◽  
Maria Castro Morera
Keyword(s):  
Mathematics Achievement ◽  
Multilevel Model ◽  
School Effectiveness ◽  
Economic Resources ◽  
School Resources ◽  
Educational Expenditures ◽  
Effectiveness Factors ◽  
Plausible Values ◽  
Level Model

This article presents a school effectiveness multilevel model based on PISA 2003 data. Our focus is math achievement. We employ a three level model (student, school and country) with variables related to school effectiveness as predictors of student achievement. We use a methodology that takes into account the multiple plausible values generated by PISA for each student. Results show that family structure, gender, family and school socioeconomic status, home educational resources, quality of school resources, student educational expenditures, school size, teacher education and country economic resources devoted to education all have sizable influence on mathematics achievement.

scholarly journals Attention-Deficit Hyperactivity Disorder in the EFL Classroom: A Case Study

2012 ◽  
Vol 13 (18) ◽  
pp. 23-33
Author(s):  
Olmedo Bula Villalobos
Keyword(s):  
High School ◽  
Attention Deficit Hyperactivity Disorder ◽  
Costa Rica ◽  
Attention Deficit ◽  
Hyperactivity Disorder ◽  
Student’S Performance

This case study reflects upon the experience of implementing a plan for a student with attention-deficit hyperactivity disorder attending a high school within an EFL environment in Costa Rica. The plan consisted of a set of four observations and four work sessions. In addition, the study reports on the strategies and techniques used with the student and the teacher in order to improve the student’s performance in the class. The results and the analysis of the case study are also discussed. One of the main findings is that this disorder can be effectively treated.

scholarly journals Analysis and Prediction of Student Academic Performance Using Machine Learning

2020 ◽  
Vol 4 (01) ◽  
pp. 10-15
Author(s):  
Ajibola Oluwafemi Oyedeji ◽  
Abdulrazaq M Salami ◽  
Olaolu Folorunsho ◽  
Olatilewa R. Abolade
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Linear Regression ◽  
Supervised Learning ◽  
Family Background ◽  
Average Error ◽  
Learning Tools ◽  
Student’S Performance ◽  
Distribution Family ◽  
Demographic Distribution

Analyzing the academic performance of students is of utmost importance for academic institutions and educationists, so as to know the ways of improving individual student’s performance. The project analyzed the past results of students including their individual attributes including age, demographic distribution, family background and attitude to study and tests this data using machine learning tools. Three models which are; Linear regression for supervised learning, linear regression with deep learning and neural network were tested using the test and train data with the Linear regression for supervised learning having the best mean average error (MAE).

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