Calibration of the Multiple-Choice Test in Genetics Using R Software
DOI:
https://doi.org/10.5281/zenodo.19151184Keywords:
Item Response Theory, 2 PL (Parameter Logistics) Model, latent traits, R SoftwareAbstract
The present research aims to calibrate the prelim multiple choice test in Genetics during the First Semester of 2016-2017 using ltm package in R Software. There was a total of 89 students enrolled in Biology 106 (Gensetics) of the Department of Biology, College of Natural Sciences and Mathematics which serves as the respondents of this study. The study made use of the descriptive research design to identify the test’s item difficulty, test’s item discrimination, test’s item fitness, and students’ ability and latent traits. An interview was also conducted among the respondents enrich and support the findings of the research. Findings showed that the prelim multiple choices in Genetics needed revision and replacement because of incorrect item keying as determined using the R Software. It was also found out that almost half of the students failed to pass the exam because they were confused in answering the test. Furthermore, the findings showed that the multiple item tests in Genetics are poorly constructed. In line with the research findings, the researchers highly recommended that teachers must construct a well-written test by applying the use of table of specification. They must also employ varied teaching strategies, techniques and models that would cater the learning needs of all their students. Furthermore, students must study their lesson and not to hesitate ask for clarification if they are confused with the lecture.
Downloads
References
An, X. & Yung, Y.F. (2014). Item Response Theory: What It Is and How You Can Use the IRT Procedure to Apply It. Paper SAS364-2014.
Atar, B. & Aktan, D. C. (2013). Person Explanatory Item Response Theory Analysis: Latent Regression Two Parameter Logistic Model. 38, 168.
Baker, F. B. (2001). The basics of item response theory. College Park, MD: ERIC Clearinghouse on Assessment and Evaluation. Original work published in 1985. Retrieved from http://echo.edres.org:8080/irt/baker/
Bastari, B. (2000). Linking multiple-choice and constructed-response items to a common proficiency.University of Massachusetts Amherst. Thesis dissertation
Bjorner, J. B., Chang, C.-H., Thissen, D., & Reeve, B. B. (2007). Developing tailored instruments: Item banking and computerized adaptive assessment. Quality of Life Research, 16, 95–108.
Brzezińska, J. (2016). Polytomous Item Response Theory Models Using R. Econometrics 2(52).
Chalmers, R. P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software May 2012, Volume 48, Issue 6.
Chen, J. (2012). Applying item response theory models to design a learning progression- based science assessment. Thesis dissertation. Michigan State University.
Colvin, K. (2015). Evaluation of R Package ltm with IRT Dichotomous Models.
University of Albany. de Ayala, R. J. (2009). The theory and practice of item response theory. New York: The Guilford Press.
Dorans, N. J., Pomerich, M., & Holland, P. W. (Eds.). (2007). Linking and aligning scores and scales. New York: Springer.
Edelen, M.O. & Reeve, B. (2007). Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement.Qual Life Res, 16:5–18
Embretson, S., & Reise, S. (2000). Item response theory for psychologists. Mahwah, N Lawrence Erlbaum.
Foley, B. (2010). Improving IRT parameter estimates with small sample sizes: Evaluating the efficacy of a new data augmentation technique. Thesis Dissertations. University of Nebraska - Lincoln.
Geremew, N.M. (2014). Applying item response theory to the survey of adult skills (PIAAC). Master thesis of statistician Stolcholm University.
Gonzalez, E.(2008). Principles of multiple matrix booklet designs and parameter recovery in largescale. Assessments. Educational Testing Service, Princeton, NJ, USA1. Indiana University, Bloomington, IN, USA2.
Griffiths, T. (n.d.).Why do students find genetics so difficult to learn.University of British Columbia Vancouver Canada.
Haberman, S J., & Sinharay, S. (2013). Assessing item fit for unidimensional item response theory models using residuals from estimated item response functions. Western Kentucky University. Psychomertica-78,3,417-440.
Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Newbury Park, CA: Sage.
Hula, W., Fergadiotis ,G.,& Martin, N. (2012). Model choice and sample size in item response theory analysis of alphasia test. American Journal Speech-Language Pathology, 21, S38-S50.
Journal of Statistical Software, (JSS) (2007).
Johns, J., Mahaven, S. & Woolf, B. (2006). Estimating student proficiency using an item response theory model. Computer Science Department University of Massachusetts. Amherst Amherst, MA 01003 U.S.A.
Kolen, M. J. and Brennan, R. L. (2011). Test Equating: Methods and Practices. New
Köse, I.A. (2014). Assessing model data fit of unidimensional item response theory simulated data. Educational Research Review, 9(17),642-649.
Le, D., Chalmers, R. P., & Liang, L. (2013). Applying item response theory modelling in educational research. Graduate Thesis and Dissertations. Paper 13410.
Lee, SH (2007). Multidimensional item response theory: A SAS MDIT MACRO and Emprical Study of PIAT MATH Test. Unpublished Doctoral Dissertation. The University of Oklahoma.
Maindonald, J. H. (2013). Using r for data analysis and graphics introduction, code and commentary. Centre for Mathematics and its Applications, Australian National University.
Mastura, F. (2016). Comparison of the three item response theory models.Thesis and Dissertations, Mindanao State University-Marawi City.
Microsoft Encarta, (2009).
Molenaar, D., Tuerlinckx, F., and van der Maas, H. L. J. (2015). Fitting Diffusion Item Response Theory Models for Responses and Response Times Using the R- Package diffIRT, Journal of Statistical Science.
Navarro, R., & Santos, R. (2012). Assessment of learning Outcomes. Lorimar Publishing, Inc. Oxford university Press, (2004).
Ryan, J., & Brockman, F. (2009). A practitioner’s introduction to equating with primers on classical test theory and item response theory. Washington, DC: Council of Chief State School Officers, State Collaborative on Assessment and Student Standards, Technical Issues in Large Scale Assessment.
Sahin, F., and Colvin, K. (2015). Evaluation of R Package ltm with IRT Dichotomous Models. NERA Conference Proceedings 2015. 6.
Smith, D.M. & Venables, W.N. (2005). An introduction to r: software for statistical modelling and computing. CSIRO Mathematical and Information Science. Cleveland, Australia.
Student Handbook, 2008 Edition.Mindanao State University.Marawi City.
Tao, W. (2008). Using the score based testlet method to handle local item dependence.
Thesis dissertation. Boston College Lynch School of Education.
Thorpe, G. L. and Favia, A. (2012). "Data Analysis Using Item Response Theory Methodology: An Introduction to Selected Programs and Applications." Psychology Faculty Scholarship.
Vocabulary.comDictionary.http://www.vocabolary.com/dictionary.htm-4/18/16.
Wu, M.,& Adams, R.,(2006). Modelling mathematics problem solving item responses using a multidimensional IRT model. Math. Educ. Res. J. 18(2)93-113.
Yang, S. (2007). A Comparison of Unidimensional and Multidimensional Rasch Models Using Parameter Estimates and Fit Indices when Assumption of Unidimensionality is violated. Unpublished Doctoral Dissertaion. The Ohio State University.
Yu, C.H. (2013). A simple guide to the item response theory (IRT) and rasch modeling.
Retrived from http://www.creative-wisdom.com-08/20/2013.
Zao,Y. (2008). Approaches for addressing the fit of item response theory models to educational test data. Unpublished Doctoral Dissetation. University of Massachusetts Ambers
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
