APLIKASI CROSS VALIDATION PADA MODEL SKILL SISWA

Authors

  • Wahyu Hartono Universitas Swadaya Gunung Djati, Indonesia

DOI:

https://doi.org/10.33603/e.v7i2.4220

Abstract

One of the activities in the educational test is making a diagnosis to determine whether or not a person's skills are present. This study specifically aims to design student skill models in basic mathematics courses and perform validation using a leave-one-out cross validation to select an accurate model. The diagnostic test questions used in this study ranged from moderate to difficult. The findings of this study indicate that the method of fixed test questions in order of questions from easy to difficult is better than the method of design of the initial fixed test questions.

 

Keywords: Bayes Network, Cross Validation, Student Skill Model, Diagnostic test

Author Biography

Wahyu Hartono, Universitas Swadaya Gunung Djati

Program Studi Pendidikan Matematika

References

Almond, Russell G dan Mislevy, Robert J. 1999. Graphical models and computerized

adaptive testing. Applied Psychological Measurement, 23(3):223–237.

Corbett, A., Anderson, J. 1995. Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction 4, 253–278.

Conati, Cristina et al. 1997. On-line student modeling for coached problem solving using Bayesian net- works. In Anthony Jameson, Cecile Paris, and Carlo Tasso, editors, Proc. of the Sixth Int. Conf. on User Modeling (UM97), Chia Laguna, Sardinia, Italy, pages 231–242. Springer Verlag.

Millan, Eva dan P´erez-de-la-Cruz, Jos´e Luis. 2002. A Bayesian diagnostic algo- rithm for student modeling and its evaluation. User modeling and User- Adapted Interaction, 12(2–3):281–330.

Mislevy, Robert J et al. 2000. Computerized Adaptive Testing: A Primer. Mahwah, N.J., Lawrence Erlbaum Associates, second edition.

Hadi, S. 2013. Pengembangan Computerized Adaptive Test Berbasis Web. Yogyakarta: Aswaja Pressindo

Vomlel, Jiri . 2004. Bayesian Networks in Educational Testing. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems, Vol. 12, Supplementary Issue 1, 2004, pp. 83-100. A draft version.

Ben-Bassat, Moshe. 1978. Myopic policies in sequential classification. IEEE Transactions on Computers, 27(2): 170–174.

Almond et al. 2001. Models for conditional probability tables in educational assessment. In Proc. of the 2001 Conference on AI and Statistics. Society for AI and Statistics.

Lauritzen, Steffen L. 1996. Graphical Models. Clarendon Press, Oxford.

Plajner, M dan Vomlel, J (2016). Student Skill Models in Adaptive Testing. JMLR: Workshop and Conference Proceedings vol 52, 403-414.

Wang, Y., Heffernan, N.T. 2012. The Student Skill Model. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 399–404. Springer, Heidelberg.

Wang Y., Beck J. (2013) Class vs. Student in a Bayesian Network Student Model. In: Lane H.C., Yacef K., Mostow J., Pavlik P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science, vol 7926. Springer, Berlin, Heidelberg

Downloads

Published

2020-07-15

Issue

Section

Artikel

Citation Check