Data-mining techniques in detecting factors linked to academic achievement

School Effectiveness and School Improvement

Volumn 28, Issue 1, 2017, Pages 39-55

  Martínez Abad, Fernando ^a   Chaparro Caso López, Alicia A ^b  

^a UNIVERSITY OF SALAMANCA   (Spain)

^b UNIVERSIDAD AUTÓNOMA DE BAJA CALIFORNIA   (Mexico)

Author keywords

academic achievement; context surveys; contextual factors; Data mining; large scale assessment

Indexed keywords

EID: 84988378137     PISSN: 09243453     EISSN: 17445124     Source Type: Journal    
DOI: 10.1080/09243453.2016.1235591     Document Type: Article

References (70)

1. Alcover, R., Benlloch, J., Blesa, P., Calduch, M. A., Celma, M., Ferri, C., … Zúnica, L. R., (2007, July). Análisis del rendimiento académico en los estudios de informática de la Universidad Politécnica de Valencia aplicando técnicas de minería de datos [Analysis of academic achievement in computer studies at the Polytechnic University of Valencia applying data-mining techniques]. Paper presented at the XIII Jornadas de Enseñanza universitaria de la Informática, Teruel, España. Retrieved from http://bioinfo.uib.es/~joemiro/aenui/procJenui/Jen2007/alanal.pdf
2. Ballesteros, A., Sánchez-Guzmán, D., & García, R., (2014). Minería de datos educativa:Una herramienta para la investigación de patrones de aprendizaje sobre un contexto educativo [Educational data mining:A research tool for learning patterns on an educational context]. Latin-American Journal of Physics Education, 7, 662–668. Retrieved from http://www.lajpe.org/dec13/22-LAJPE_814_bis_Alejandro_Ballesteros.pdf
3. Baradwaj, B. K., & Pal, S., (2011). Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications, 2(6), 63–69.
4. Barbero, I., Holgado, F. P., Villa, E., & Chacón, S., (2007). Actitudes, hábitos de estudio y rendimiento en Matemáticas:Diferencias por género [Attitudes, study habits and performance in mathematics:Gender differences]. Psicothema, 19, 413–421.
5. Bryk, A. S., & Raudenbush, S. W., (1992). Hierarchical linear models:Applications and data analysis methods. Newbury Park, CA:Sage.
6. Campbell, D. T., & Stanley, J. C., (1963). Experimental and quasi-experimental designs for research. Chicago, IL:Wadsworth.
7. Cardoso, A. P., Ferreira, M., Abrantes, J. L., Seabra, C., & Costa, C., (2011). Personal and pedagogical interaction factors as determinants of academic achievement. Procedia–Social and Behavioral Sciences, 29, 1596–1605. doi:10.1016/j.sbspro.2011.11.402
8. Carter, C. L., & Hamilton, H. J., (1995). A fast, on-line generalization algorithm for knowledge discovery. Applied Mathematics Letters, 8(2), 5–11. doi:10.1016/0893-9659(95)00002-8
9. Caso, J., Chaparro, A., Díaz, C., & Urias, E., (2012). Propiedades psicométricas de las escalas, cuestionarios e inventarios de la Estrategia Evaluativa Integral 2011:Factores asociados al aprendizaje [Psychometric properties of the scales, questionnaires and inventories of the Evaluative Comprehensive Strategy 2011:Factors associated with learning] (UEE Technical Report 12-002). Retrieved from http://uee.uabc.mx/uee/nivel1.php?p=estudiosYproyectos/reportesTecnicos
10. Caso, J., & Hernández, L., (2010). Modelo explicativo del bajo rendimiento escolar:Un estudio con adolescentes mexicanos [Explanatory model of educational underachievement:A study of Mexican adolescents]. Revista Iberoamericana de Evaluación Educativa, 3(2),145–159.
11. Castro, M., & Lizasoain, L., (2012). Las técnicas de modelización estadística en la investigación educativa:Minería de datos, modelos de ecuaciones estructurales y modelos jerárquicos lineales [Statistical modeling techniques in educational research:Data mining, structural equation modeling and hierarchical linear models]. Revista Española de Pedagogía, 70, 131–148.
12. Cervini, R., Dari, N., & Quiroz, S., (2014). Estructura familiar y rendimiento académico en países de América Latina:Los datos del segundo estudio regional comparativo y explicativo [Family structure and academic performance in Latin America:Data from the second regional comparative and explanatory study]. Revista Mexicana de Investigación Educativa, 19, 569–597.
13. Charemza, W. W., (1991). Large econometric models of an East European economy:A critique of the methodology. Economic Modelling, 8, 45–62. doi:10.1016/0264-9993(91)90021-F
14. Chen, S. Y., & Liu, X., (2005). Data mining from 1994 to 2004:An application-orientated review. International Journal of Business Intelligence and Data Mining, 1, 4–21. doi:10.1504/IJBIDM.2005.007315
15. Cohen, J., (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46. doi:10.1177/001316446002000104
16. Contreras, L. A., Rodríguez, J. C., Caso, J., Díaz, C., & Urias, E., (2012). Estrategia evaluativa integral 2011:Factores asociados al aprendizaje de estudiantes de primero, segundo y tercero de secundaria en Baja California [Comprehensive assessment strategy 2011:Factors associated with student learning first, second and third high school in Baja California] (Technical report UEE 12-001). Retrieved from http://uee.uabc.mx/uee/nivel1.php?p=estudiosYproyectos/reportesTecnicos
17. Cools, W., De Fraine, B., Van den Noortgate, W., & Onghena, P., (2009). Multilevel design efficiency in educational effectiveness research. School Effectiveness and School Improvement, 20, 357–373. doi:10.1080/09243450902850176
18. Cordero, J. M., & Manchón, C., (2014). Factores explicativos del rendimiento en educación primaria:Un análisis a partir de TIMSS 2011 [Explanatory factors for achievement in primary education:An analysis using TIMSS 2011]. Estudios sobre Educación, 27, 9–35.
19. DeFreitas, K., & Bernard, M., (2015). Comparative performance analysis of clustering techniques in educational data mining. IADIS International Journal on Computer Science and Information Systems, 10(2), 65–78.
20. De Witte, K., & Kortelainen, M., (2013). What explains the performance of students in a heterogeneous environment? Conditional efficiency estimation with continuous and discrete environmental variables. Applied Economics, 45, 2401–2412. doi:10.1080/00036846.2012.665602
21. Diniz, A., Dias, M., & Silva, L., (2011). Cognitive abilities, sociocultural background and academic achievement. Psicothema, 23, 695–700.
22. Ecker, K., & Dacharay, G., (2013, June). Aplicación de técnicas de Minería de Datos al análisis de situación y comportamiento académico de alumnos [Application of data-mining techniques to the analysis of situation and academic performance of students]. Paper presented at the XV Workshop de investigadores en ciencias de la computación, Paraná, Argentina.
23. Goldhaber, D., & Chaplin, D. D., (2015). Assessing the “Rothstein Falsification Test”:Does it really show teacher value-added models are biased? Journal of Research on Educational Effectiveness, 8, 8–34. doi:10.1080/19345747.2014.978059
24. González, C., Caso, J., Díaz, K., & López, M., (2012). Academic performance and associated factors. Contributions from some large-scale assessments. Bordón, 64(2), 51–68. Retrieved from http://iide.ens.uabc.mx/blogs/jcaso/files/2012/09/PDF1.pdf
25. Guimarães, J., & Sampaio, B., (2013). Family background and students’ achievement on a university entrance exam in Brazil. Education Economics, 21, 38–59.
26. Haile, G. A., & Nguyen, A. N., (2008). Determinants of academic attainment in the United States:A quantile regression analysis of test scores. Education Economics, 16, 29–57.
27. Hall, M. A., (1999). Correlation-based feature subset selection for machine learning (Doctoral thesis). Retrieved from http://www.cs.waikato.ac.nz/~mhall/thesis.pdf
28. Han, J., Pei, J., & Kamber, M., (2011). Data mining:Concepts and techniques (3rd ed.). Amsterdam, The Netherlands:Elsevier.
29. Heck, R. H., & Moriyama, K., (2010). Examining relationships among elementary schools’ contexts, leadership, instructional practices, and added-year outcomes:A regression discontinuity approach. School Effectiveness and School Improvement, 21, 377–408. doi:10.1080/09243453.2010.500097
30. Hojo, M., (2012). Determinants of academic performance in Japan. Japanese Economy, 39(3), 3–29. doi:10.2753/JES1097-203X390301
31. Houtsma, M., & Swami, A., (1995). Set-oriented data mining in relational databases. Data and Knowledge Engineering, 17, 245–262. doi:10.1016/0169-023X(95)00024-M
32. Hsieh, M., (2013). Data mining from education databases examine the factors impacting the school performance in the United States. International Journal of Intelligent Technologies and Applied Statistics, 6, 135–143. doi:10.6148/IJITAS.2013.0602.03
33. Kaighobadi, M., & Allen, M. T., (2008). Investigating academic success factors for undergraduate business students. Decision Sciences Journal of Innovative Education, 6, 427–436.
34. Kerlinger, F. N., & Lee, H. B., (1999). Foundations of behavioral research (4th ed.). Fort Worth, TX:Wadsworth.
35. King, G., McDougall, J., DeWit, D., Hong, S., Miller, L., Offord, D., … LaPorta, J., (2005). Pathways to children’s academic performance and prosocial behaviour:Roles of physical health status, environmental, family, and child factors. International Journal of Disability, Development and Education, 52, 313–344.
36. Kiray, S. A., Gok, B., & Bozkir, A. S., (2015). Identifying the factors affecting science and mathematics achievement using data mining methods. Journal of Education in Science, Environment and Health (JESEH), 1, 28–48.
37. Kristjánsson, A. L., & Sigfúsdóttir, I. D., (2009). The role of parental support, parental monitoring, and time spent with parents in adolescent academic achievement in Iceland:A structural model of gender differences. Scandinavian Journal of Educational Research, 53, 481–496.
38. Lee, J., & Shute, V. J., (2010). Personal and social-contextual factors in K-12 academic performance:An integrative perspective on student learning. Educational Psychologist, 45, 185–202. doi:10.1080/00461520.2010.493471
39. Liem, G. A. D., Martin, A. J., Porter, A. L., & Colmar, S., (2012). Sociocultural antecedents of academic motivation and achievement:Role of values and achievement motives in achievement goals and academic performance. Asian Journal of Social Psychology, 15, 1–13. doi:10.1111/j.1467-839X.2011.01351.x
40. Luan, J., (2002). Data mining and its applications in higher education. New Directions for Institutional Research, 2002(113), 17–36.
41. Ma, X., (2005). Growth in mathematics achievement:Analysis with classification and regression trees. The Journal of Educational Research, 99, 78–86.
42. Mahimuang, S., (2005). Factors influencing academic achievement and improvement:A value-added approach. Educational Research for Policy and Practice, 4, 13–26. doi:10.1007/s10671-005-0677-1
43. Mannila, H., (2000). Theoretical frameworks for data mining. ACM SIGKDD Explorations Newsletter, 1(2), 30–32.
44. Marquez, J., Shack-Marquez, J., & Wascher, W. L., (1985). Statistical inference, model selection and research experience. A multinomial model of data mining. Economics Letters, 18, 39–44. doi:10.1016/0165-1765(85)90075-8
45. Martínez, F., Chaparro, A. A., & Lizasoain, L., (2014). The socioeconomic index in the analysis of large-scale assessments:Case study in Baja California (Mexico). In Proceedings TEEM’ 14. Technological Ecosystems for Enhancing Multiculturality (pp. 461–467). Salamanca, Spain:ACM.
46. Mih, V., & Mih, C., (2013). Perceived autonomy-supportive teaching, academic self-perceptions and engagement in learning:Toward a process model of academic achievement. Cognition, Brain, Behavior, 17, 289–313.
47. Miñano, P., Castejón, J.-L., & Gilar, R., (2012). An explanatory model of academic achievement based on aptitudes, goal orientations, self-concept and learning strategies. The Spanish Journal of Psychology, 15, 48–60.
48. Morgan, J., Daugherty, R., Hilchie, A., & Carey, B., (2003). Sample size and modeling accuracy of decision tree based data mining tools. Journal of Management Information and Decision Sciences, 6(2), 71–99.
49. Nath, S. R., (2012). Factors influencing primary students’ learning achievement in Bangladesh. Research in Education, 88, 50–63.
50. Nghe, N. T., Janecek, P., & Haddawy, P., (2007). A comparative analysis of techniques for predicting academic performance. In 37th ASEE/IEEE Frontiers in Education Conference (pp. T2G-7–T2G-12). Retrieved from http://www.researchgate.net/publication/224299912_A_comparative_analysis_of_techniques_for_predicting_academic_performance
51. Oates, T., & Jensen, D., (1997, June). The effects of training set size on decision tree complexity machine learning. In ICML ’97 Proceedings of the Fourteenth International Conference on Machine Learning (pp. 254–262). San Francisco, CA:Morgan Kaufmann.
52. Osmanbegović, E., & Suljić, M., (2012). Data mining approach for predicting student performance. Economic Review:Journal of Economics and Business, 10(1), 3–12.
53. Payandeh Najafabadi, A. T., Omidi Najafabadi, M., & Farid-Rohani, M. R., (2013). Factors contributing to academic achievement:A Bayesian structure equation modelling study. International Journal of Mathematical Education in Science and Technology, 44, 490–500.
54. Porcel, E. A., Dapozo, G. N., & López, M. V., (2010). Predicción del rendimiento académico de alumnos de primer año de la FACENA (UNNE) en función de su caracterización socioeducativa [Prediction of academic performance of first-grade students from FACENA (UNNE) depending on their socio educative characterization]. Revista Electrónica de Investigación Educativa, 12(2). Retrieved from http://redie.uabc.mx/redie/article/view/264/730
55. Quinlan, J. R., (1986). Induction of decision trees. Machine Learning, 1, 81–106. doi:10.1023/A:1022643204877
56. Quinlan, J. R., (1993). C4.5:Programs for machine learning. San Francisco, CA:Morgan Kaufmann.
57. Risso, A., Peralbo, M., & Barca, A., (2010). Cambios en las variables predictoras del rendimiento escolar en Enseñanza Secundaria [Changes in the predictors of school performance in secondary education]. Psicothema, 22, 790–796.
58. Romero, C., Ventura, S., & García, E., (2008). Data mining in course management systems:Moodle case study and tutorial. Computers and Education, 51, 368–384. doi:10.1016/j.compedu.2007.05.016
59. Rosário, P., Nuñez, J. C., González-Pineda, J. A., Almeida, L., Soares, S., & Rubio, M., (2005). El aprendizaje escolar examinado desde la perspectiva del “Modelo 3P” de J. Biggs [School learning examined from the perspective of “Model 3P” of J. Biggs]. Psicothema, 17, 20–30.
60. Rygielski, C., Wang, J.-C., & Yen, D. C., (2002). Data mining techniques for customer relationship management. Technology in Society, 24, 483–502. doi:10.1016/S0160-791X(02)00038-6
61. Santos, M. A., Godás, A., & Lorenzo, M., (2013). Rendimiento académico y diversidad cultural:El eje lingüístico [Academic achievement and cultural diversity:Linguistic axis]. Revista Española de Pedagogía, 71, 461–478.
62. Schumacher, P., Olinsky, A., Quinn, J., & Smith, R., (2010). A comparison of logistic regression, neural networks, and classification trees predicting success of actuarial students. Journal of Education for Business, 85, 258–263.
63. Strayhorn, T. L., (2010). The role of schools, families, and psychological variables on math achievement of Black high school students. The High School Journal, 93, 177–194.
64. Stull, J., (2013). Family socioeconomic status, parent expectations, and a child’s achievement. Research in Education, 90, 53–67.
65. Tejedor, F. J., (2003). Poder explicativo de algunos determinantes del rendimiento en los estudios universitarios [Explanatory power of some determinants of performance in university studies]. Revista Española de Pedagogía, 61(224), 5–32.
66. Tekin, A., (2014). Early prediction of students’ grade point averages at graduation:A data mining approach. Eurasian Journal of Educational Research, 54, 207–226.
67. Tsai, M.-H., & Liu, F.-Y., (2013). Multigroup structural equation approach:Examining the relationship among family socioeconomic status, parent-child interaction, and academic achievement using TASA samples. International Journal of Intelligent Technologies and Applied Statistics, 6, 353–373. doi:10.6148/IJITAS.2013.0604.03
68. Vandamme, J.-P., Meskens, N., & Superby, J.-F., (2007). Predicting academic performance by data mining methods. Education Economics, 15, 405–419.
69. Win, R., & Miller, P. W., (2005). The effects of individual and school factors on university students’ academic performance. The Australian Economic Review, 38, 1–18
70. Yildirim, S., (2012). Teacher support, motivation, learning strategy use, and achievement:A multilevel mediation model. The Journal of Experimental Education, 80, 150–172.