Educational Data Mining: Identification of factors associated with school effectiveness in PISA assessment
暂无分享,去创建一个
Adriana Gamazo | Fernando Martínez-Abad | María-José Rodríguez-Conde | Adriana Gamazo | F. Martínez-Abad | M. Rodriguez-Conde | M. Rodríguez-Conde
[1] Nicola F. Kirby,et al. Using Decision Tree Analysis to Understand Foundation Science Student Performance. Insight Gained at One South African University , 2014 .
[2] L. Angus. The Sociology of School Effectiveness , 1993 .
[3] Antoni Verger,et al. The growth and spread of large-scale assessments and test-based accountabilities: a political sociology of global education reforms , 2018, Educational Review.
[4] Dominik Petko,et al. Perceived Quality of Educational Technology Matters , 2017 .
[5] H. Hill. The Coleman Report, 50 Years On: What Do We Know about the Role of Schools in Academic Inequality? , 2017 .
[6] Y. Cheng,et al. School autonomy, leadership and learning: a reconceptualisation , 2016 .
[7] Kevin Casey,et al. Utilizing student activity patterns to predict performance , 2017, International Journal of Educational Technology in Higher Education.
[8] S. Kuger,et al. Increased instruction hours and the widening gap in student performance , 2017 .
[9] A. Fernández-Cano. Una crítica metodológica a las evaluaciones PISA , 2016 .
[10] Stephen Alstrup,et al. High-School Dropout Prediction Using Machine Learning: A Danish Large-scale Study. , 2015 .
[11] P. Róbert,et al. School Choice in the Light of the Effectiveness Differences of Various Types of Public and Private Schools in 19 OECD Countries , 2008 .
[12] J. Martínez,et al. Determinantes del riesgo de fracaso escolar en España en PISA-2009 y propuestas de reforma , 2013 .
[13] H. Goldstein. Multilevel Statistical Models , 2006 .
[14] David A Chambers,et al. Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias , 2014, Clinical and Translational Science.
[15] Danhui Zhang,et al. A multilevel analysis of the effects of disciplinary climate strength on student reading performance , 2018 .
[16] Ersoy Öz,et al. Identifying the Classification Performances of Educational Data Mining Methods: A Case Study for TIMSS , 2017 .
[17] U. Bronfenbrenner. The Ecology of Human Development: Experiments by Nature and , 1979 .
[18] Cheng Yong Tan,et al. Information technology, mathematics achievement and educational equity in developed economies , 2017 .
[19] Patrícia Costa,et al. Skilled Students and Effective Schools: Reading Achievement in Denmark, Sweden, and France , 2018 .
[20] J. Scheerens. Process indicators of school functioning: A selection based on the research literature on school effectiveness , 1991 .
[21] Patrícia Costa,et al. Can low skill teachers make good students? Empirical evidence from PIAAC and PISA , 2015 .
[22] John Jerrim,et al. To weight or not to weight?: the case of PISA data , 2017 .
[23] Xiufeng Liu,et al. Using Data Mining to Predict K-12 Students' Performance on Large-Scale Assessment Items Related to Energy. , 2008 .
[24] A. Gamoran,et al. Equality of Educational Opportunity A 40 Year Retrospective , 2007 .
[25] Sam Stringfield,et al. Outlier Studies of School Effectiveness , 1994 .
[26] S. Han. School-based teacher hiring and achievement inequality: A comparative perspective , 2018, International Journal of Educational Development.
[27] Peter D. Turney. Technical note: Bias and the quantification of stability , 1995, Machine Learning.
[28] Fernando Martínez Abad,et al. Data-mining techniques in detecting factors linked to academic achievement , 2017 .
[29] M. Luque,et al. Balancing Teachers’ Math Satisfaction and Other Indicators of the Education System’s Performance , 2016 .
[30] M. V. Alderete,et al. Acceso a las TIC y rendimiento educativo: ¿una relación potenciada por su uso? Un análisis para España , 2017 .
[31] Danhui Zhang,et al. How Does ICT Use Influence Students' Achievements in Math and Science over Time? Evidence from PISA 2000 to 2012. , 2016 .
[32] Elvira Carpintero Molina,et al. ¿Cuánto oro hay entre la arena? Minería de datos con los resultados de España en PISA 2015 , 2018 .
[33] Hüseyin Gürüler,et al. A new student performance analysing system using knowledge discovery in higher educational databases , 2010, Comput. Educ..
[34] Seiji Isotani,et al. Educational Data Mining: A review of evaluation process in the e-learning , 2018, Telematics Informatics.
[35] D. Caro,et al. Performance status and change – measuring education system effectiveness with data from PISA 2000–2009 , 2014 .
[36] F. Martínez-Abad. Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach , 2019, Front. Psychol..
[37] Mohammed J. Zaki,et al. Predicting Math Performance from Raw Large-Scale Educational Assessments Data : A Machine Learning Approach , 2016 .
[38] Parental involvement and pupil reading achievement in Ireland: Findings from PIRLS 2011 , 2015 .
[39] Understanding School Effects in South Africa Using Multilevel Analysis: Findings from TIMSS 2011. , 2015 .
[40] David Kaplan,et al. The Methodology of PISA: Past, Present, and Future , 2016 .
[41] K. Choi,et al. A comparative investigation of the presence of psychological conditions in high achieving eighth graders from TIMSS 2007 Mathematics , 2012 .
[42] Jaan Mikk,et al. Relationships Between Student Perception of Teacher-Student Relations and PISA Results in Mathematics and Science , 2016 .
[43] Josip Burušić,et al. School Effectiveness: An Overview of Conceptual, Methodological and Empirical Foundations , 2016 .
[44] Jui-Long Hung,et al. Integrating Data Mining in Program Evaluation of K-12 Online Education , 2012, J. Educ. Technol. Soc..
[45] Elvira Carpintero Molina,et al. How much gold is in the sand? Data mining with Spain’s PISA 2015 results , 2018 .
[46] Sebastián Ventura,et al. Predicting students' final performance from participation in on-line discussion forums , 2013, Comput. Educ..
[47] Dennis Niemann,et al. PISA and Its Consequences: Shaping Education Policies through International Comparisons. , 2017 .
[48] Martina R. M. Meelissen,et al. The contribution of TIMSS to the link between school and classroom factors and student achievement , 2013 .
[49] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.
[50] Mingjie Tan,et al. Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method , 2015, Int. J. Emerg. Technol. Learn..
[51] Dmitri Rozgonjuk,et al. To what extent does Internet use affect academic performance? Using Evidence from the large-scale PISA study. , 2017 .
[52] P. Banerjee,et al. A systematic review of factors linked to poor academic performance of disadvantaged students in science and maths in schools , 2016 .
[53] Javier Murillo. School Effectiveness Research in Latin America , 2007 .
[54] L. Kyriakides,et al. Using a multidimensional approach to measure the impact of classroom-level factors upon student achievement: a study testing the validity of the dynamic model , 2008 .
[55] James Sebastian,et al. The relationship of school-based parental involvement with student achievement: a comparison of principal and parent survey reports from PISA 2012 , 2017 .
[56] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .
[57] Jingjing Zhang,et al. How the ICT development level and usage influence student achievement in reading, mathematics, and science , 2015, Comput. Educ..
[58] Melinda Whitford,et al. Opportunities-to-Learn at Home: Profiles of Students With and Without Reaching Science Proficiency , 2011 .
[59] Marshall S. Smith,et al. Effective Schools: A Review , 1983, The Elementary School Journal.
[60] Jo-Anne Baird,et al. Lessons Learned from PISA: A Systematic Review of Peer-Reviewed Articles on the Programme for International Student Assessment , 2018 .
[61] J. Teodorović. Student background factors influencing student achievement in Serbia , 2012 .
[62] Lawrence W. Lezotte. School improvement based on the effective schools research , 1989 .
[63] Baldoino Fonseca dos Santos Neto,et al. Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses , 2017, Comput. Hum. Behav..