Un modelo TRI de múltiples facetas para la evaluación del desempeño docente en el aula
dc.contributor.advisor | Montenegro Díaz, Alvaro Mauricio | spa |
dc.contributor.author | Cordoba Perozo, Karen Rosana | spa |
dc.contributor.researchgroup | SICS Research Group | spa |
dc.date.accessioned | 2020-08-13T14:45:21Z | spa |
dc.date.available | 2020-08-13T14:45:21Z | spa |
dc.date.issued | 2020-06-19 | spa |
dc.description.abstract | Teacher evaluation in higher education has been a controversial topic and its use is frequent in this field for taking decision. In most of cases, the evaluation is based on the assumption that students learn more from highly qualified teachers and its validity is based on the fact that students observe the teachers performances in the classroom. Therefore, the students should be the evaluators under the assumption that they will respond sincerely when asked about the teacher performance. However, many studies question about the methodologies used for getting such measurements, in general, because the averages by categorical responses have little statistical sense. In this document, the measurement of teaching performance is proposed through a multi-faceted TRI model that takes into account parameters associated with the severity of the evaluator and an additional parameter that is related to the effect of the evaluated course. For the model estimation, Bayesian inference techniques were used due to the large number of parameters to be estimated. The proposal was applied to a data set obtained from a survey of perception of teaching performance conducted by the Faculty of Science of the National University of Colombia to students of the same faculty. The proposed model was evaluated with goodness of fit statistics and compared with a model that did not include the additional parameter. The results obtained indicate that the proposed model had a better fit and the selection criteria indicate that the proposed model was more appropriate for the measurement. Additionally, using auxiliary variables, differences were observed between the qualifications given by female students and male students and the qualifications by postgraduate students and undergraduate students, in addition, there were differences between the estimated average difficulties of the courses according to the study area that offers. | spa |
dc.description.abstract | La evaluación del desempeño docente en la educación superior ha sido un tema controversial y de frecuente uso en este campo para la toma de decisiones. En la mayoría de casos, se basan en el supuesto de que los estudiantes aprenden más de profesores altamente calificados y su validez se sustenta en el hecho de que los estudiantes observan el desempeño de los docentes en el salón de clase. Por lo tanto, deberían ser ellos los evaluadores bajo el supuesto que responderán sinceramente cuando sean preguntados por el desempeño del docente. Sin embargo, muchos estudios ponen en duda las metodologías usadas para dichas mediciones, en general, porque los promedios de respuestas categóricas tienen poco sentido estadístico. En este documento, se propone la medición del desempeño docente a través de un modelo de TRI de múltiples facetas que tiene en cuenta parámetros asociados a la severidad del evaluador y un parámetro adicional que está relacionado con el efecto del curso evaluado. Para la estimación del modelo se hizo uso de técnicas de inferencia bayesiana debido a la gran cantidad de parámetros a estimar. La propuesta se aplicó a un conjunto de datos obtenido de una encuesta de percepción de desempeño docente realizada por la Facultad de Ciencias de la Universidad Nacional de Colombia a estudiantes de la misma durante el año 2015. El modelo propuesto se evaluó con estadísticas de bondad de ajuste y se comparó con un modelo que no contempla el parámetro adicional. Los resultados obtenidos indican que el modelo propuesto presentó mejor ajuste y los criterios de selección indican que el modelo propuesto es más apropiado para la medición. Adicionalmente, haciendo uso de variables auxiliares se observaron diferencias entre las calificaciones otorgadas por estudiantes mujeres y estudiantes hombres y por estudiantes pertenecientes a programas de posgrado y de pregrado, además, se observaron diferencias entre las dificultades promedio estimadas de los cursos según el área de estudio. | spa |
dc.description.additional | Línea de Investigación: Teoría de Respuesta al Ítem | spa |
dc.description.degreelevel | Maestría | spa |
dc.format.extent | 71 | spa |
dc.format.mimetype | application/pdf | spa |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/78018 | |
dc.language.iso | spa | spa |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | spa |
dc.publisher.department | Departamento de Estadística | spa |
dc.publisher.program | Bogotá - Ciencias - Maestría en Ciencias - Estadística | spa |
dc.relation.references | Bélanger, C. H. & Longden, B. (2009). The effective teacher’s characteristics as perceived by students, Tertiary Education and Management 15(4): 323–340. | spa |
dc.relation.references | Box, G. E. (1980). Sampling and bayes’ inference in scientific modelling and robustness, Journal of the Royal Statistical Society: Series A (General) 143(4): 383–404. | spa |
dc.relation.references | Braga, M., Paccagnella, M. & Pellizzari, M. (2014). Evaluating students’ evaluations of professors, Economics of Education Review 41: 71–88. | spa |
dc.relation.references | Cameletti, M. & Caviezel, V. (2012). The cronbach-mesbah curve for assessing the unidimensionality of an item set: The r package cmc. | spa |
dc.relation.references | Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Bru-baker, M., Guo, J., Li, P. & Riddell, A. (2017). Stan: A probabilistic programming language, Journal of statistical software 76(1). | spa |
dc.relation.references | Casella, G. & George, E. I. (1992). Explaining the gibbs sampler, The American Statistician 46 (3): 167–174. | spa |
dc.relation.references | Centra, J. A. (1993). Reflective Faculty Evaluation: Enhancing Teaching and Determining Faculty Effectiveness. The Jossey-Bass Higher and Adult Education Series., ERIC. | spa |
dc.relation.references | Centra, J. A. & Creech, F. R. (1976). The relationship between student teachers and course characteristics and student ratings of teacher effectiveness, Project Report, Princeton,NJ, Educational Testing Service, pp. 76–1. | spa |
dc.relation.references | Chen, M.-H., Shao, Q.-M. & Ibrahim, J. G. (2012). Monte Carlo methods in Bayesian computation, Springer Science & Business Media. | spa |
dc.relation.references | Clayson, D. E. (2009). Student evaluations of teaching: Are they related to what students learn? a meta-analysis and review of the literature, Journal of Marketing Education 31(1): 16–30. | spa |
dc.relation.references | Cohen, P. A. (1981). Student ratings of instruction and student achievement: A meta-analysis of multisection validity studies, Review of educational Research 51(3): 281–309. | spa |
dc.relation.references | Cranton, P. A. & Smith, R. A. (1986). A new look at the effect of course characteristics on student ratings of instruction, American Educational Research Journal 23(1): 117–128. | spa |
dc.relation.references | Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests, psychometrika 16(3): 297–334. | spa |
dc.relation.references | de Andrade, D. F., Tavares, H. R. & da Cunha Valle, R. (2000). Teoria da resposta ao item: conceitos e aplicações, ABE, Sao Paulo. | spa |
dc.relation.references | Eckes, T. (2011). Introduction to many-facet rasch measurement, Frankfurt: Peter Lang. | spa |
dc.relation.references | Feldman, K. A. (1977). Consistency and variability among college students in rating theirteachers and courses: A review and analysis, Research in Higher Education 6(3): 223–274. | spa |
dc.relation.references | Feldman, K. A. (1978). Course characteristics and college students’ ratings of their teachers: What we know and what we don’t, Research in Higher Education 9(3): 199–242. | spa |
dc.relation.references | Feldman, K. A. (1979). The significance of circumstances for college students’ ratings of their teachers and courses, Research in Higher Education 10(2): 149–172. | spa |
dc.relation.references | Feldman, K. A. (1983). Seniority and experience of college teachers as related to evaluations they receive from students, Research in Higher Education 18(1): 3–124. | spa |
dc.relation.references | Feldman, K. A. (1987). Research productivity and scholarly accomplishment of college teachers as related to their instructional effectiveness: A review and exploration, Research in higher education 26(3): 227–298 | spa |
dc.relation.references | Feldman, K. A. (1989). The association between student ratings of specific instructional dimensions and student achievement: Refining and extending the synthesis of data from multisection validity studies, Research in Higher education 30(6): 583–645. | spa |
dc.relation.references | Feldman, K. A. (1992). College students’ views of male and female college teachers: Part (i) —evidence from the social laboratory and experiments, Research in Higher Education 33(3): 317–375 | spa |
dc.relation.references | Fox, J.-P. (2010). Bayesian item response modeling: Theory and applications, Springer Science & Business Media. | spa |
dc.relation.references | Galbraith, C. S., Merrill, G. B. & Kline, D. M. (2012). Are student evaluations of teaching effectiveness valid for measuring student learning outcomes in business related classes? a neural network and bayesian analyses, Research in Higher Education 53(3): 353–374. | spa |
dc.relation.references | Gelfand, A. E., Dey, D. K. & Chang, H. (1992). Model determination using predictive distributions with implementation via sampling-based methods, Technical report, Stanford Univ CA Dept of Statistics | spa |
dc.relation.references | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian data analysis, Chapman and Hall/CRC. | spa |
dc.relation.references | Gelman, A., Hwang, J. & Vehtari, A. (2014). Understanding predictive information criteriafor bayesian models, Statistics and computing 24(6): 997–1016 | spa |
dc.relation.references | Gelman, A., Lee, D. & Guo, J. (2015). Stan: A probabilistic programming language for bayesian inference and optimization, Journal of Educational and Behavioral Statistics 40(5): 530–543. | spa |
dc.relation.references | Gelman, A., Meng, X.-L. & Stern, H. (1996). Posterior predictive assessment of model fitness via realized discrepancies, Statistica sinica pp. 733–760. | spa |
dc.relation.references | Gelman, A., Rubin, D. B. et al. (1992). Inference from iterative simulation using multiple sequences, Statistical science 7(4): 457–472. | spa |
dc.relation.references | Geman, S. & Geman, D. (1984). Stochastic relaxation, gibbs distributions, and the bayesian restoration of images, IEEE Transactions on pattern analysis and machine intelligence (6): 721–741. | spa |
dc.relation.references | Griewank, A. & Walther, A. (2008). Evaluating derivatives: principles and techniques of algorithmic differentiation, Vol. 105, Siam. | spa |
dc.relation.references | Hambleton, R. K., Swaminathan, H. & Rogers, H. J. (1991). Fundamentals of item response theory, Vol. 2, Sage. | spa |
dc.relation.references | Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications,Biometrika 57(1): 97–109. | spa |
dc.relation.references | Hoffman, M. D. & Gelman, A. (2014). The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo., Journal of Machine Learning Research 15(1): 1593–1623 | spa |
dc.relation.references | Jolliffe, I. T. (2002). Principal components in regression analysis, Principal component analysis pp. 167–198. | spa |
dc.relation.references | Koushki, P. A. & Kunh, H. A. J. (1982). How realiable are student evaluations of teachers?, Engineering Education 72: 362–367 | spa |
dc.relation.references | Lord, F. M. & Novick, M. R. (2008). Statistical theories of mental test scores, IAP. | spa |
dc.relation.references | Luo, Y. & Jiao, H. (2018). Using the stan program for bayesian item response theory, Educational and psychological measurement 78 (3): 384–408. | spa |
dc.relation.references | Marsh, H. W. (1987). Students’ evaluations of university teaching: Research findings, methodological issues, and directions for future research, International journal of educational research 11(3): 253–388. | spa |
dc.relation.references | Marsh, H. W. (2007). Students’ evaluations of university teaching: Dimensionality, reliability, validity, potential biases and usefulness, The scholarship of teaching and learning in higher education: An evidence-based perspective, Springer, pp. 319–383. | spa |
dc.relation.references | Martin, E. (1984). Power and authority in the classroom: Sexist stereotypes in teaching evaluations, Signs: Journal of Women in Culture and Society 9(3): 482–492. | spa |
dc.relation.references | Masters, G. N. (1982). A rasch model for partial credit scoring, Psychometrika 47(2): 149–174. | spa |
dc.relation.references | Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. & Teller, E. (1953). Equation of state calculations by fast computing machines, The journal of chemical physics 21(6): 1087–1092. | spa |
dc.relation.references | Metropolis, N. & Ulam, S. (1949). The monte carlo method, Journal of the American statistical association 44(247): 335–341. | spa |
dc.relation.references | Murray, H. G. (2005). Student evaluation of teaching: Has it made a difference, Annual Meeting of the Society for Teaching and Learning in Higher Education. Charlottetown, Prince Edward Island. | spa |
dc.relation.references | Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods, Department of Computer Science, University of Toronto, Toronto, ON, Canada. | spa |
dc.relation.references | Neal, R. M. et al. (2011). Mcmc using hamiltonian dynamics, Handbook of markov chain monte carlo 2(11): 2. | spa |
dc.relation.references | Ostini, R. & Nering, M. L. (2006). Polytomous item response theory models, number 144, Sage. | spa |
dc.relation.references | Perry, R. P., Niemi, R. R. & Jones, K. (1974). Effect of prior teaching evaluations and lecture presentation on ratings of teaching performance., Journal of Educational Psychology 66(6): 851. | spa |
dc.relation.references | Roberts, G. O. (1996). Markov chain concepts related to sampling algorithms, Markov chain Monte Carlo in practice 57: 45–58. | spa |
dc.relation.references | Small, A. C., Hollenbeck, A. R. & Haley, R. L. (1982). The effect of emotional state on student ratings of instructors, Teaching of Psychology 9(4): 205–211. | spa |
dc.relation.references | Spencer, P. A. & Flyr, M. L. (1992). The formal evaluation as an impetus to classroom change: Myth or reality?. Riverside, CA Research/Technical Report | spa |
dc.relation.references | Spiegelhalter, D. J., Best, N. G., Carlin, B. P. & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit, Journal of the royal statistical society: Series b (statistical methodology) 64(4): 583–639. | spa |
dc.relation.references | Stan (2018). Stan Modeling Language Users Guide and Reference Manual. Version 2.18.0. URL: http://mc-stan.org | spa |
dc.relation.references | Stark, P. & Freishtat, R. (2014). An evaluation of course evaluations, ScienceOpen Re-search. | spa |
dc.relation.references | Uttl, B., Eche, A., Fast, O., Mathison, B., Valladares Montemayor, H. & Raab,V. (2012). Student evaluation of instruction/teaching (sei/set) review, Calgary, AB, Canada: Mount Royal Faculty Association Retrieved from: http://mrfa.net/files/MRFASEIReviewv6. pdf. | spa |
dc.relation.references | Uttl, B., White, C. A. & Gonzalez, D. W. (2017). Meta-analysis of faculty’s teaching effectiveness: Student evaluation of teaching ratings and student learning are not related, Studies in Educational Evaluation 54: 22–42. | spa |
dc.relation.references | Van Der Linden, W. J. & Hambleton, R. K. (1997). Item response theory: Brief history, common models, and extensions, Handbook of modern item response theory, Springer, pp. 1–28. | spa |
dc.relation.references | Vehtari, A., Gelman, A. & Gabry, J. (2017). Practical bayesian model evaluation using leave-one-out cross-validation and waic, Statistics and computing 27(5): 1413–1432. | spa |
dc.relation.references | Wachtel, H. K. (1998). Student evaluation of college teaching effectiveness: A brief review, Assessment & Evaluation in Higher Education 23(2): 191–212. | spa |
dc.relation.references | Watanabe, S. (2010). Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory, Journal of Machine Learning Research 11(Dec): 3571–3594. | spa |
dc.relation.references | Wolfe, E. W. & Dobria, L. (2008). Applications of the multifaceted rasch model, Best practices in quantitative methods pp. 71–85. | spa |
dc.relation.references | Abrami, P. C., Perry, R. P. & Leventhal, L. (1982). The relationship between student personality characteristics, teacher ratings, and student achievement., Journal of Educational Psychology 74(1): 111. | spa |
dc.relation.references | Aleamoni, L. M. (1981). Student ratings of instruction, inJ. Millman (ed.), Handbook of Teacher Evaluation, Beverly Hills, Calif.: Sage Publications, Beverly Hills, pp. 110–145. | spa |
dc.relation.references | Andrich, D. (1978). A rating formulation for ordered response categories, Psychometrika 43(4): 561–573. | spa |
dc.relation.references | Ariyo, O., Quintero, A., Muñoz, J., Verbeke, G. & Lesaffre, E. (2019). Bayesian model selection in linear mixed models for longitudinal data, Journal of Applied Statistics pp. 1–24. | spa |
dc.relation.references | Bartholomew, D. J., Knott, M. & Moustaki, I. (2011). Latent variable models and factor analysis: A unified approach, Vol. 904, John Wiley & Sons. | spa |
dc.relation.references | Basow, S. A. & Silberg, N. T. (1987). Student evaluations of college professors: Are female and male professors rated differently?, Journal of educational psychology 79(3): 308. | spa |
dc.relation.references | Becker, W. E. & Watts, M.(1999). How departments of economics evaluate teaching, American Economic Review 89(2): 344–349. | spa |
dc.rights | Derechos reservados - Universidad Nacional de Colombia | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.license | Atribución-NoComercial 4.0 Internacional | spa |
dc.rights.spa | Acceso abierto | spa |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | spa |
dc.subject.ddc | 519 - Probabilidades y matemáticas aplicadas | spa |
dc.subject.ddc | 378 - Educación superior (Educación terciaria) | spa |
dc.subject.proposal | modelos de TRI de múltiples facetas | spa |
dc.subject.proposal | multi-faceted TRI model | eng |
dc.subject.proposal | teacher performance | eng |
dc.subject.proposal | desempeño docente | spa |
dc.subject.proposal | bayesian inference | eng |
dc.subject.proposal | inferencia bayesiana | spa |
dc.subject.proposal | Educación | spa |
dc.subject.proposal | Education | eng |
dc.title | Un modelo TRI de múltiples facetas para la evaluación del desempeño docente en el aula | spa |
dc.title.alternative | A multi-faceted TRI model for the evaluation of teacher performance in the classroom | spa |
dc.type | Trabajo de grado - Maestría | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |