Made Duananda Kartika Degeng
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Development of An LMS-Based Self-Efficacy-Oriented E-Module for Junior High School Students’ Remembering and Understanding in Social Studies Rysky Indra Prasta; Muhibuddin Fadhli; Made Duananda Kartika Degeng; Yerry Soepriyanto
Proceedings Series of Educational Studies 2026: International Conference on Educational Management and Technology (5th ICEMT 2026)
Publisher : Universitas Negeri Malang

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This study aimed to develop and examine the feasibility, practicality, and effectiveness of a Learning Management System (LMS)-based Social Studies e-module oriented toward students’ self-efficacy. The e-module addressed eruption mitigation in mountainous areas and its relation to the Sustainable Development Goals (SDGs), while responding to the limited use of interactive LMS features in the learning context. The study employed a Research and Development (R&D) approach using the ADDIE model, comprising analysis, design, development, implementation, and evaluation. The product was developed as a Heyzine flipbook, visually designed in Figma, and integrated into the mooc.um.ac.id LMS to organize learning materials, activities, feedback, and assessment. Expert validation produced an average score of 99% (M = 3.96), indicating very high feasibility, while student responses reached M = 3.44 (86%), indicating very positive acceptance. Learning outcomes were assessed using pretest and posttest data. The mean N-Gain was 0.858, categorized as high, and the pretest–posttest difference was statistically significant (p < 0.001) with a large effect size (d = 1.60). Students’ self-efficacy reached a mean of 3.09 on a four-point scale. These findings indicate substantial improvement in the targeted cognitive outcomes following implementation and support the feasibility and practical value of integrating a structured e-module with an LMS. Because the trial used a one-group pretest–posttest design, the findings should be interpreted as evidence from a development trial rather than definitive causal evidence. The study contributes a practical design that combines LMS-based delivery, interactive learning activities, and self-efficacy-oriented scaffolding for contextualized Social Studies learning.
Mapping Students' Artificial Intelligence Usage Profiles in Computer Programming Courses: A Quantitative Descriptive Study Yerry Soepriyanto; Henry Praherdhiono; Agus Wedi; Made Duananda Kartika Degeng; Asrofi Asrofi; Dany Wijaya Kesuma
Proceedings Series of Educational Studies 2026: International Conference on Educational Management and Technology (5th ICEMT 2026)
Publisher : Universitas Negeri Malang

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The advent of artificial intelligence (AI) has transformed the way students learn programming in recent years; however, a clear picture of how this technology is actually used on a day-to-day basis has rarely been systematically mapped. This study aims to map the artificial intelligence usage profiles of undergraduate students in Educational Technology within the Visual Programming course. A quantitative descriptive method was employed, involving 172 students as respondents. Data were collected via a 23-item Likert-scale questionnaire (1–5), and subsequently categorised using Azwar’s formula to determine low, moderate and high levels of usage. The results indicate that ChatGPT is the most widely used AI tool amongst students, accounting for 76.2% of respondents. Students use AI intensively, primarily to deepen their understanding of concepts they have not yet grasped, rather than merely to complete tasks instantly. Overall, students exhibited a high-category AI usage profile, with 77.3% of all research subjects falling into this category. These findings suggest that students do not merely use AI passively, but rather utilise it as an active and reflective learning companion; this can therefore serve as a basis for universities to formulate more targeted AI integration policies within their visual programming.