Implementasi Item Response Theory Model Three-Parameter Logistics Pada Aplikasi Computerized Adaptive Test

  • Muhammad Ruslan Maulani Politeknik Pos Indonesia
  • Supriady Supriady Politeknik Pos indonesia

Abstract

Paper and Pencils Test (PPTs) merupakan ujian yang memberikan pertanyaan dengan tingkat kesulitan yang sama kepada peserta ujian. Akibatnya, tes menjadi lebih lama dan pertanyaan yang diberikan tidak informatif sehingga peserta ujian merasa kesulitan atau merasa terlalu mudah dalam mengerjakannya. Tujuan dari penelitian ini yaitu membuat aplikasi sistem ujian berbasis komputer yang dapat mengukur kemampuan peserta tes, dimana tingkat kesukaran butir soal disesuaikan dengan tingkat kemampuan dari peserta tes. Metode penelitian yang dilakukan dalam penelitian ini terdiri dari beberapa tahap, yaitu: tahap persiapan, tahap analisis dan perancangan, tahap implementasi dan pengujian, serta tahap pelaporan. Sedangkan metode yang digunakan untuk melakukan proses estimasi terhadap butir soal dengan menggunakan metode Maximum Likelihood Estimation (MLE) dan metode yang digunakan untuk sistem Computerized Adaptive Test (CAT) yaitu metode Item Response Theory (IRT) model logistik three parameter logistic (3PL). Hasil dari penelitian ini yaitu aplikasi sistem CAT berbasis web yang dapat dijadikan sebagai sistem ujian online dan alat ukur untuk menentukan kemampuan (ability) peserta ujian

References

[1] C. Wang and H. Lu, “Mediating effects of individuals’ ability levels on the relationship of Reflective-Impulsive cognitive style and item response time in CAT,” Educ. Technol. Soc., vol. 21, no. 4, 2018.
[2] R. Conejo, E. Guzmán, and M. Trella, “The SIETTE Automatic Assessment Environment,” Int. J. Artif. Intell. Educ., vol. 26, no. 1, 2016, doi: 10.1007/s40593-015-0078-4.
[3] Norlaila, “Efektivitas Evaluasi Pembelajaran di Sekolah Dasar Islam Terpadu (SDIT) Ukhuwah Kota Banjarmasin,” Tashwir, vol. 3, no. 5, 2015.
[4] G. Lotito and G. Pirlo, “Item response theory for optimal questionnaire design,” J. E-Learning Knowl. Soc., vol. 9, no. 3, 2013, doi: 10.20368/1971-8829/820.
[5] C. H. Lan, S. Graf, K. R. Lai, and K. Kinshuk, “Enrichment of peer assessment with agent negotiation,” IEEE Trans. Learn. Technol., vol. 4, no. 1, 2011, doi: 10.1109/TLT.2010.30.
[6] C. Romero and S. Ventura, “Educational data mining and learning analytics: An updated survey,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 10, no. 3, 2020, doi: 10.1002/widm.1355.
[7] S. Oppl, F. Reisinger, A. Eckmaier, and C. Helm, “A flexible online platform for computerized adaptive testing,” Int. J. Educ. Technol. High. Educ., vol. 14, no. 1, 2017, doi: 10.1186/s41239-017-0039-0.
[8] E. Istiyono, W. S. B. Dwandaru, R. Setiawan, and I. Megawati, “Developing of computerized adaptive testing to measure physics higher order thinking skills of senior high school students and its feasibility of use,” Eur. J. Educ. Res., vol. 9, no. 1, 2020, doi: 10.12973/eu-jer.9.1.91.
[9] M. Rezaie and M. Golshan, “Computer Adaptive Test (CAT): Advantages and Limitations,” Int. J. Educ. Investig. Available online @ www.ijeionline.com, vol. 2, no. 5, 2015.
[10] S. Coronado, S. Sandoval-Bravo, P. L. Celso-Arellano, and A. Torres-Mata, “Competitive learning using a three-parameter logistic model,” Eur. J. Contemp. Educ., vol. 7, no. 3, 2018, doi: 10.13187/ejced.2018.3.448.
[11] I. Pedrosa, J. Suárez-Álvarez, E. García-Cueto, and J. Muñiz, “A computerized adaptive test for enterprising personality assessment in youth,” Psicothema, vol. 28, no. 4, 2016, doi: 10.7334/psicothema2016.68.
[12] L. Peute, T. Scheeve, and M. Jaspers, “Classification and regression tree and computer adaptive testing in cardiac rehabilitation: Instrument validation study,” J. Med. Internet Res., vol. 22, no. 1, 2020, doi: 10.2196/12509.
[13] Á. Postigo, M. Cuesta, I. Pedrosa, J. Muñiz, and E. García-Cueto, “Development of a computerized adaptive test to assess entrepreneurial personality,” Psicol. Reflex. e Crit., vol. 33, no. 1, 2020, doi: 10.1186/s41155-020-00144-x.
[14] A. L. Zenisky and R. M. Luecht, “The future of computer-based testing: Some new paradigms.,” in Educational measurement: From foundations to future., 2016.
[15] M. D. Nieto et al., “Calibrating a new item pool to adaptively assess the Big Five,” Psicothema, vol. 29, no. 3, 2017, doi: 10.7334/psicothema2016.391.
[16] E. Peña-Suárez, F. Menéndez, F.-P. Eduardo, and J. Muñiz, “Computerized Adaptive Assessment of Organizational Climate,” An. Psicol., vol. 33, no. 1, 2016, doi: 10.6018/analesps.32.3.225921.
[17] K. Scalise and D. D. Allen, “Use of open-source software for adaptive measurement: Concerto as an R-based computer adaptive development and delivery platform,” Br. J. Math. Stat. Psychol., vol. 68, no. 3, 2015, doi: 10.1111/bmsp.12057.
[18] Y. L. P. Vega, J. C. G. Bolanos, G. M. F. Nieto, and S. M. Baldiris, “Application of item response theory (IRT) for the generation of adaptive assessments in an introductory course on object-oriented programming,” 2012, doi: 10.1109/FIE.2012.6462377.
[19] S. Shafiee, Y. Wautelet, L. Hvam, E. Sandrin, and C. Forza, “Scrum versus Rational Unified Process in facing the main challenges of product configuration systems development,” J. Syst. Softw., vol. 170, 2020, doi: 10.1016/j.jss.2020.110732.
[20] U. M. Language, Unified Modeling Language, v2.5.1, no. Version 2.5.1. 2017.
[21] K. Krisna, I. K. R. Arthana, and G. A. Pradnyana, “Pengujian Usability Pada Prototype Aplikasiwadaya Dengan Metode Usability Testing Mengadopsi Standar Iso 9241:11,” Ultim. J. Tek. Inform., vol. 11, no. 1, 2019, doi: 10.31937/ti.v11i1.1240.
Published
2022-04-30
How to Cite
MAULANI, Muhammad Ruslan; SUPRIADY, Supriady. Implementasi Item Response Theory Model Three-Parameter Logistics Pada Aplikasi Computerized Adaptive Test. Jurnal Ilmiah Media Sisfo, [S.l.], v. 16, n. 1, p. 1-9, apr. 2022. ISSN 2527-7340. Available at: <http://ejournal.stikom-db.ac.id/index.php/mediasisfo/article/view/1117>. Date accessed: 24 mar. 2023. doi: https://doi.org/10.33998/mediasisfo.2022.16.1.1117.