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Peluang dan Tantangan Pengintegrasian Learning Management System (LMS) dalam Pembelajaran Matematika di Indonesia Ibnu Rafi; Fina Fitri Nurjannah; Iqlima Ramadhani Fabella; Sri Andayani
Jurnal Tadris Matematika Vol 3 No 2 (2020)
Publisher : Institut Agama Islam Negeri (IAIN) Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21274/jtm.2020.3.2.229-248

This literature review aimed to describe the opportunities and challenges of integrating the Learning Management System (LMS) in mathematics learning, especially in terms of the non-technical aspects. Because many LMS platforms which can be integrated into learning process, this review only discusses the three LMS platforms which are common, namely Moodle, Edmodo, and Schoology. This review was conducted with the method as proposed by Templier and Paré (2015), in which the literature materials consisted of journal articles, proceedings, and official websites of LMS. The results of this review showed that integrating the LMS in mathematics learning indicated opportunities in terms of the positive impact which can be obtained from integrating the LMS. On the other hand, the identified challenges were about how mathematics content or materials were organized through integrating the LMS, providing feedback, and maximizing the online discussion facility provided by LMS. Therefore, to obtain the positive impact from integrating the LMS into mathematics learning, teachers should pay attention to these three challenges.

Factors Influencing the Success of Students’ Learning through Online Learning/Distance Education : A Bibliometric Analysis of Scopus Database Agus Santoso; Heri Retnawati; Munaya Nikma Rosyada; Ezi Apino; Ibnu Rafi; Kartianom Kartianom; Aigul Dauletkulova
Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Vol 10, No 1 (2024): March
Publisher : Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v10i1.10856

This study aims to examine the co-occurrence of several topics linked to factors that influence student learning success and explore the motor themes related to factors that influence student learning success. This study used the literature review method with data analysis using R Studio and VOSViewer software. Data analyzed were exported from the Scopus website from 2013 to 2023 of 383 documents. The research data analysis techniques used network mapping analysis of VOSviewer results (network visualization) and content analysis within the keywords and studies. The findings of this study presented the keywords (most frequent words, trending topics), co-occurrence network (thematic map and thematic evolution), and discussion of topics related to factors that influence student learning success. The basic themes identified were higher education and Covid-19. Meanwhile, gender and self-efficacy are motor themes that could be researched further as those can influence student learning performance in online learning. The findings of the bibliometric analysis were intended to reveal unique insights into the factors that determine student learning performance in distant learning and contribute to previously unexplored issues.

Peluang dan Tantangan Pengintegrasian Learning Management System (LMS) dalam Pembelajaran Matematika di Indonesia Ibnu Rafi; Fina Fitri Nurjannah; Iqlima Ramadhani Fabella; Sri Andayani
Jurnal Tadris Matematika Vol 3 No 2 (2020)
Publisher : Universitas Islam Negeri Sayyid Ali Rahmatullah Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21274/jtm.2020.3.2.229-248

This literature review aimed to describe the opportunities and challenges of integrating the Learning Management System (LMS) in mathematics learning, especially in terms of the non-technical aspects. Because many LMS platforms which can be integrated into learning process, this review only discusses the three LMS platforms which are common, namely Moodle, Edmodo, and Schoology. This review was conducted with the method as proposed by Templier and Paré (2015), in which the literature materials consisted of journal articles, proceedings, and official websites of LMS. The results of this review showed that integrating the LMS in mathematics learning indicated opportunities in terms of the positive impact which can be obtained from integrating the LMS. On the other hand, the identified challenges were about how mathematics content or materials were organized through integrating the LMS, providing feedback, and maximizing the online discussion facility provided by LMS. Therefore, to obtain the positive impact from integrating the LMS into mathematics learning, teachers should pay attention to these three challenges.

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

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

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.

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