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All Journal Jurnal Inovasi Teknologi Pendidikan Proceedings Series of Educational Studies
Setiasih, Muhti
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Education

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Correlation of Learning Style and Learning Motivation on Learning Outcomes Understanding Concepts Setiasih, Muhti; Degeng, Made Duananda Kartika; Kurniawan, Citra
Proceedings Series of Educational Studies 2024: The 3rd International Conference on Educational Management and Technology (ICEMT) 2024
Publisher : Universitas Negeri Malang

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Abstract

The aim of this research is to determine the relationship between learning style and learning motivation with students' learning outcomes in understanding concepts in the Teaching and Learning course at the State University of Malang. This research was conducted using the Multiple Correlation Test simultaneously and the Pearson Correlation Test. From the results of data processing it is known that sig. (2-tailed) learning style and learning outcome variables are 0.084 and sig. (2-tailed) on the learning motivation and learning outcomes variables is 0.718. Meanwhile, the results of simultaneous multiple correlation analysis test data processing on sig. F Change is worth 0.219. The findings from this research indicate that there is no relationship between learning style and learning motivation and students' conceptual understanding in the Teaching and Learning course at the State University of Malang. This can be caused by several factors such as a less supportive learning environment, differences in educationa
From logs to insights: A comprehensive framework for data-driven learning insights Soepriyanto, Yerry; Nugroho, Rengga Prakoso; Nahri, Mochammad Hilman Amirudin; Kesuma, Dany Wijaya; Setiasih, Muhti
Jurnal Inovasi Teknologi Pendidikan Vol. 12 No. 1 (2025): March
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jitp.v12i1.77432

Abstract

This study develops a theoretical framework for learning analytics utilizing data from the Moodle Learning Management System (LMS). Despite Moodle's extensive use in educational settings, its potential for learning analytics remains underutilized. This research aims to design a predictive framework for identifying learning difficulties through Moodle's internal analytics, incorporating various data points such as activity completion, attendance logs, social interactions, and learner habits. The study employs a research and development methodology with three main stages: (1) needs analysis and learning component identification, (2) theoretical framework design, and (3) validation through focused group discussions with learning experts. The framework integrates predictive modeling for learning retention, task load analysis, and personalized learning style assessments based on the VARK model. Results demonstrate that the framework effectively uses Moodle's default logs for analyzing learner behavior, although it is limited to online interactions within the LMS. Validation confirms its alignment with Moodle's architecture and online learning theories, with minor adjustments for task load components. The framework offers a scalable solution for institutions managing large student populations and varied learning models, serving as a foundation for early intervention and improved learning outcomes. Future studies could expand the framework's scope to include offline and face-to-face interactions.
Co-Authors Citra Kurniawan Kesuma, Dany Wijaya Made Duananda Kartika Degeng Nahri, Mochammad Hilman Amirudin Rengga Prakoso Nugroho Yerry Soepriyanto