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All Journal DIKSI CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Riset Pendidikan Matematika Al-Jabar : Jurnal Pendidikan Matematika REiD (Research and Evaluation in Education) Psychology, Evaluation, and Technology in Educational Research Indonesian Journal of Educational Assesment Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran International Journal of Robotics and Control Systems
Rosyada, Munaya Nikma
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Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : CAUCHY: Jurnal Matematika Murni dan Aplikasi

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Deep-Rasch as an Alternative to Rasch Modeling under Assumption Violations and Small Sample Sizes Santoso, Agus; Afendi, Farit Mochamad; Pardede, Timbul; Retnawati, Heri; Rafi, Ibnu; Apino, Ezi; Rosyada, Munaya Nikma
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.36276

Abstract

In certain situations, it may be challenging to fully exploit the advantages of modern test theory, including Rasch modeling and item response theory (IRT), when applied to real data. Although Rasch modeling tends to be more robust than IRT for small sample sizes, it still requires that the assumptions of unidimensionality and local independence be satisfied. In practice, these assumptions are often violated, which can lead to less accurate analyses and reduced validity of the results. Deep-Rasch, which integrates deep learning with Rasch modeling, has been proposed as an alternative measurement framework to overcome these limitations. This study examines the potential of Deep-Rasch as an alternative to Rasch modeling using student response data from 17 final semester examinations at Universitas Terbuka (UT), with sample sizes ranging from 33 to 11,504 students. Most examinations consisted of 30 multiple-choice items. The analyses showed that several datasets violated one or both assumptions of Rasch modeling. Nevertheless, Deep-Rasch performed comparably to conventional Rasch modeling in estimating item difficulty and student ability parameters, as well as in predicting student responses. Remarkably, for the smallest sample size (\emph{n} = 33), Deep-Rasch exhibited slightly better performance than Rasch modeling.
Co-Authors Abd Hamid, Harris Shah Agus Santoso Apino, Ezi Atymtaevna, Begimbetova Guldana Dauletkulova, Aigul Deni Hadiana, Deni Diki, Diki Djidu, Hasan Elly Arliani Farit Mochamad Afendi Heri Retnawati Himmawati Puji Lestari Jailani Jailani Jamhari Jamhari Kana Hidayati Kardanova, Elena Kartianom, Kartianom Kassymova, Gulzhaina K Kassymova, Gulzhaina K. Kassymova, Gulzhaina Kuralbayevna Lantip Diat Prasojo M. Rafi Mathilda Susanti Muhardis Muhardis Pardede, Timbul Rahayu, Dyah Paminta Samsul Hadi Septinda Rima Dewanti Sotlikova, Rimajon Sudji Munadi Sulistyaningsih, Eny Sumaryanto Sumaryanto, Sumaryanto Tuanaya, Rugaya Yusron, Eri