STATISTICAL EVALUATION OF EXAMINATION TESTS IN MATHEMATICS FOR ECONOMISTS

Authors

  • Nikola Kaspříková University of Economics, Prague

DOI:

https://doi.org/10.7160/eriesj.2012.050403

Keywords:

Binomial distribution, entrance examinations, examples, multiple choice question tests, probability.

Abstract

Examination results are rather important for many students with regard to their future profession development. Results of exams should be carefully inspected by the teachers to help improve design and evaluation of tests and education process in general. Analysis of examination papers in mathematics taken by students of basic mathematics course at University of Economics in Prague is reported. The first issue addressed is identification of significant dependencies between performance in particular problem areas covered in the test and also between particular items and total score in test or ability level as a latent trait. The assessment is first performed with Spearman correlation coefficient, items in the test are then evaluated within Item Response Theory framework. The second analytical task addressed is a search for groups of students who are similar with respect to performance in test. Cluster analysis is performed using partitioning around medoids method and final model selection is made according to average silhouette width. Results of clustering, which may be also considered in connection with setting of the minimum score for passing the exam, show that two groups of students can be identified. The group which may be called "well-performers" is the more clearly defined one.

References

  • Cronbach, L.J. (2004) ‘My Current Thoughts on Coefficient Alpha and Successor Procedures’, Educational and Psychological Measurement, vol. 64, no. 3, pp. 391-418.

  • Harik, P., Clauser, B.E., Grabovsky, I., Nungester, R.J., Swanson, D., Nandakumar, R. (2009) ‘An Examination of Rater Drift Within a Generalizability Theory Framework’, Journal of Educational Measurement, vol. 46, no. 1, pp. 43-58.

  • Holland, P.W., Hoskens, M. (2003) ‘Classical test theory as a first-order item response theory: Application to true-score prediction from a possibly nonparallel test’, Psychometrika, vol. 68, no. 1, pp. 123-149.

  • Hornik, K., Leisch, F. (2004) ‘R version 2.1.0‘, Computational Statistics, vol. 20, no. 2, pp. 197-202.

  • Kaspříková, N. (2011) ‘Multivariate Analysis of Examination Papers’, Efficiency and Responsibility in Education, Proceedings of the 8th International Conference, Prague, pp. 120–127.

  • Kaspříková, N. (2012) ‘Data analysis of students' performance’, Efficiency and Responsibility in Education, Proceedings of the 9th International Conference, Prague, pp. 213–218.

  • Kotsiantis, S., Pierrakeas, C., Pintelas, P. (2004) ‘Predicting students' performance in distance learning using machine learning techniques’, Applied Artificial Intelligence, vol. 18, no. 5, pp. 411-426.

  • R Development Core Team (2012) R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/.

  • Rizopoulos, D. (2006) ‘ltm: An R Package for Latent Variable Modeling and Item Response Theory Analyses’, Journal of Statistical Software, vol. 17, no. 5, pp. 1-25.

  • Romero, C., Ventura, S. (2007) ‘Educational data mining: A survey from 1995 to 2005’, Expert Systems with Applications, vol. 33, pp. 135-146.

  • Rousseeuw, P.J. (1987) ‘Silhouettes - A Graphical Aid to the Interpretation and Validation of Cluster-analysis’, Journal of Computational and applied Mathematics, vol. 20, pp. 53-65.

  • Sheng, Y.Y. and Wikle, C.K. (2008) ‘Bayesian multidimensional IRT models with a hierarchical structure‘, Educational and Psychological Measurement, vol. 68, no. 3, pp. 413-430.

  • Sireci, S.G., Robin, F. (1999) ‘Using cluster analysis to facilitate standard setting’, Applied Measurement in Education, vol. 12, no. 3, pp. 301-325.

  • Struyf, A., Hubert. M., Rousseeuw, P.J. (1997) ‘Integrating Robust Clustering Techniques in S-PLUS’, Computational Statistics and Data Analysis, vol. 26, pp. 17-37.

  • Additional Files

    Published

    2012-12-30

    How to Cite

    Kaspříková, N. (2012) ’STATISTICAL EVALUATION OF EXAMINATION TESTS IN MATHEMATICS FOR ECONOMISTS’, Journal on Efficiency and Responsibility in Education and Science, vol. 5, no. 4, pp. 203–211. https://doi.org/10.7160/eriesj.2012.050403

    Issue

    Section

    Research Paper