Bibliometric Analysis of Articles on Computerized Adaptive Testing

Year 2021, Volume: 8 Issue: 4, 426 - 438, 01.12.2021

Meltem Yurtçu , Cem Güzeller

https://doi.org/10.17275/per.21.98.8.4

Cited By: 7

Abstract

The items that are suitable for everyone's own ability level with the support of computer programs instead of paper and pencil tests may help students to reach more accurate results. Computer adaptive tests (CAT), which are developed based on certain assumptions in this direction, are to create an optimum test for every person taking the exam. It then becomes essential to examine the development process of such important exams and to monitor what studies have contributed to this development in what year. Citespace is a program developed to map information fields, explain the relationship between different disciplines, examine and estimate the studies in a certain period of time, uncover the latest studies and predict the trend issues that occur according to the analysis of bibliographic records of related publications. In this study, it is aimed to find out what articles about CAT are produced in which areas, at what time periods e and which articles have a significant effect in these periods. CiteSpace program was used to make a document/article co-citation analysis. Articles on CAT between 1946-2016 were scanned by “or” connector. A total of 637 articles were used, the analyses were finalized according to the networks. As a result of the research, clusters were determined based on the relationship in the citations, articles that were the most cited and important among studies on CAT were presented

Keywords

Bibliometric analysis, Computerized adaptive testing, Co-citiation, Detecting and Visualizing

References

• Agapiou, A. & Lysandrou, V. (2015). Remote sensing archaeology: tracking and mapping evolution in European scientific literature from 1999 to 2015. Journal of Archaeological Science, 4, 192–200
• Archambault, E., Campbell, D., Gingras, Y. & Larivière, V. (2009). Comparing bibliometric statistics obtained from the Web of Science and Scopus. Journal of The Amerıcan Socıety For Informatıon Scıence And Technology, 60(7):1320–1326.
• Besimoğlu, C. (2015). Türkiye’deki ziraat fakültelerinin tarımsal araştırma eğilimleri: 1996-2011 yıllarının bibliyometrik analizi. (Yayınlanmamış Doktora Tezi), Hacettepe Üniversitesi Sosyal Bilimler Enstitüsü, Ankara.
• Babcock, B. & Weiss, D.J., (2012). Termination criteria in computerized adaptive tests: do variable-length CAT’s provide efficient and effective measurement? International Association for Computerized Adaptive Testing, 1, 1-18.
• Barrada, J. R.,Olea, J.,Ponsoda, V. & Abad, F., J. (2010), A method for the comparison of ıtem selection rules in computerized adaptive testing, Applied Psychological Measurement 34(6) 438–452.
• Cella, D., Yount, S. Rothrock, N., Gerson, R., Cook, K., Reeve, B., Ader, D., Fries, J. F., Bruce, B., Rose, M. (2007). The Patient-Reported Outcomes Measurment Information System (PROMIS): progress of an NIH roadmap cooperative group during its first 2 years. Medical Care, 45, 3-11
• Chen C,(2006). CiteSpaceII. Detecting and visualizing emerging trend sandtransient patterns inscientific literature. Journal of The Amerıcan Socıety For Informatıon Scıence And Technology, 57(3):359–377
• Chen, C. (2014). The CiteSpace manual. Retrieved from http://cluster.ischool.drexel.edu/~cchen/citespace.
• Cheng, Y. (2009). Computerized adaptive testing for cognitive diagnosis. In D. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing. Retrieved from www.psych.umn.edu/psylabs/CATCentral/
• Choi, S. W., Grady, M.W. & Dodd, B.G.(2011). A new stopping rule for computerized adaptive testing. Educational and Psychological Measurement, 71, 37-53.
• Deng, H., Ansley, T. & Chang, H. (2010). Stratified and maximum ınformation ıtem selection procedures in computer adaptive testing. Journal of Educational Measurement, 47(2), 202-226.
• Eroğlu, M. G. (2013). Bireyselleştirilmiş bilgisayarlı test uygulamalarında farklı sonlandırma kurallarının ölçme kesinliği ve test uzunluğu açısından karşılaştırılması (Doctoral dissertation), Hacettepe University, Ankara, Turkey.
• Feng, F., Zhang, L., Du,Y. & Wang, W. (2015). Visualization and quantitative study in bibliographicdatabases: A case in the field of university–industry cooperation. Journal of Informetrics 9, 118–134
• Fingerman, S. (2006). Web of Science and Scopus: Current features and capabilities. Issues in Science and Technology Librarianship, 48(Fall). Retrieved from http://www.istl.org/06-fall/electronic2.html
• Gierl, M. J., Lai, H. & Li, J. (2013): Identifying differential item functioning in multi-stage computer adaptive testing, Educational Research and Evaluation: An International Journal on Theory and Practice, 19(2-3), 188-203.
• Gmür, M. (2003). Co-citation analysis and the search for invisible colleges: A methodological evaluation. Scientometrics, 57(1), 27-57.
• González-Betanzos, F., Abad, F. J. & Barrada, J. R. (2014). Fixed item parameter calibration for assessing differential item functioning in computerized adaptive tests. Psicológica, 35, 331-359.
• Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of Item Response Theory. Newbury Park, CA: Sage Publications.
• Han, K. (2010). Comparision of Non-Fisher Information Item Selection Criteria in Fixed Length Computerized Adaptive Testing. Paper presented at the Annual Meeting of the National Council on Measurement in Education, Denver.
• Han, K. (2012). SimulCAT: Windows Application That Simulates Computerized Adaptive Test Administration. Applied Psychological Measurement, 36(1).64-66
• He, W., Diao, Q. & Hauser, C. (2014). A Comparison of Four Item-Selection Methods for Severely Constrained CATs. Educational and Psychological Measurement, 74(4), 677-696. doi: doi 10.1177/0013164413517503
• Huebner, A. (2010). An Overview of Recent Developments in Cognitive Diagnostic Computer Adaptive Assessments. Practical Assessment, Research & Evaluation, 15(3).1-7.
• Ho T. & Dodd, B. G. (2012). Item Selection and Ability Estimation Procedures for a Mixed-Format Adaptive Test, Applied Measurement in Education, 25(4), 305-326, doı:10.1080/08957347.2012.714686
• Hsu, C.-L. & Wang, W.C. (2015). Variabl-length computerized adaptive testing using the higer order DINA model. Journal of Educational Measurment, 52(2), 125-143. http://thomsonreuters.com/thomson-reuters-web-of-science/〉.
• Jacso, P. (2005). As we may search – Comparison of major features of the Web of Science, Scopus, and Google Scholar citation-based and citation-enhanced databases. Current Scıence, 89(9), 1537-1547.
• Kalender, İ. (2011). Effects of Different Computerized Adaptive Testing Strategies on Recovery of Ability. Unpublished Doctoral Dissertation. Middle East Technical University, Ankara.
• Khan, B. S. & e Niazi, M. A. (2017). Network community detection: A review and visual survey. CoRR, abs/1708.00977.
• Kezer, F. (2013). Bilgisayar ortamında bireye uyarlanmış test stratejilerinin karşılaştırılması. Yayınlamamış Doktora Tezi. Ankara: Ankara Üniversitesi Eğitim Bilimleri Enstitüsü
• Kim, M. C & Chen, C. (2015). A scientometric review of emerging trends and newdevelopments in recommendation systems. Scientometrics, 104, 239–263
• Lee, H. Y., & Dodd, B. G. (2012). Comparison of exposure controls, item pool characteristic, and population distributions for cat using the partial credit model. Educational and Psychological Measurement, 72(1), 159- 175. doi: 10.1177/0013164411411296
• Liu, Z., Yin, Y., Liu, W. & Dunford, M. (2015). Visualizing the intellectual structure and evolution of innovation systems research: a bibliometric analysis. Scientometrics, 103,135–158, doi:10.1007/s11192-014-1517-y
• Mongeon, P. & Paul-Hus, A. (2016) The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics, 106(1), 213-228. doi:10.1007/s11192-015-1765-5. Piromsombat, C. (2014). Differential item functioning in computerized adaptive testing: can CAT self-adjust enough?. the University of Minnesota Digital Conservancy, Retrieved from: http://hdl.handle.net/11299/163281.
• Tsay, M.-Y., Xu, H. & Wu, C.-W. (2003). Author co-citation analysis of semiconductor literatüre. Scientometrics, 58(3), 529-545.
• Su, Y.-H. (2016). A comparison of constrained ıtem selection methods in multidimensional computerized adaptive testing. Applied Psychological Measurement, 40(5),1–15. doi: 10.1177/0146621616639305
• Seyedghorban, Z., Jekanyika_matanda, M. & LaPlaca, P. (2015). Advancing theory and knowledge in the business-to-business branding literature, Journal of Business Research. 69(8), 2664-2677, doi:10.1016/j.jbusres.2015.11.002
• Santos, A.-B. (2015). Open Innovation research: trends and influences – a bibliometric analysis. Journal of Innovation Management, 3(2), 131-165
• Sulak, S. (2013). Bireyselleştirilmiş bilgisayarlı test uygulamalarında kullanılan madde seçme yöntemlerinin karşılaştırılması (Doctoral dissertation, Hacettepe University, Ankara, Turkey.
• Smits, N., Cuijpers, P. & van Straten, A. (2011). Applying computerized adaptive testing to the CES-D scale: A simulation study. Psychiatry Research 188, 147–155.
• Van Raan, A.F.J.,(2005). For your citations only? Hot topics in bibliometric analysis. Measurement: Interdisciplinary Research and Perspectives 3 (1), 50–62.
• van der Linden, W. J. & Xiong, X. (2013). Speededness And Adaptive Testing. Journal of Educational and Behavioral Statistics, 38, 418-438.
• Veldkamp, B. P. (2010). Bayesian item selection in constrained adaptive testing using shadow tests. Psicologica, 31(1), 149-169
• Yao, L. (2013). Comparing the performance of five multidimensional CAT selection procedures with different stopping rules. Appled Psychological Measurment, 37, 3-23.
• Yu, Y.-C., Chang, S.-H. & Yu, L.-C. (2016). An academic trend in STEM education from bibliometric and co-citation method. International Journal of Information and Education Technology, 6(2), 113-116, doi: 10.7763/IJIET.2016.V6.668
• Wainer, H. (1993). Some practical considerations when converting a linearly administrated test to an adaptive format. Educational Measurement: Issues and Practices, 12, 15-20.
• Weiss, D.J., & Kingsbury, G.G. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement, 21:4 361-375
• Wise, S. L., Plake, B. S, Johnsn P. L. & Roos, L. L (1992). A comparison of self-adapted and computerized adaptive tests . Journal of Educational Measurement, 29(4), 329-339
• Zhao, R. & Wang, J. (2011). Visualizing the research on pervasive and ubiquitous computing. Scientometrics, 86(3),593–612