Article Per Year (5 Year)
p-Index From 2021 - 2026
0.702
P-Index
This Author published in this journals
All Journal Journal of Informatics and Vocational Education
Rhezwan Dhaifullah Romdhoni
Universitas Pendidikan Indonesia
Computer Science & IT Education Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
The Effectiveness of Fast- vs. Slow-Tempo Music on Students’ Cognitive Performance: A Within-Subject Experimental Design Muhammad Rafly Juliawan Fernandes; Rizki Hikmawan; Rhezwan Dhaifullah Romdhoni
Journal of Informatics and Vocational Education Vol. 9 No. 2 (2026): Journal of Informatics and Vocational Education - July
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i2.3478

Abstract

This study examined the effects of music tempo on students’ cognitive performance under three conditions: fast-tempo, slow-tempo, and no music. Despite widespread use of music during studying, it remains empirically unclear whether and how music tempo differentially affects cognitive performance among junior high school students in Indonesia. A quantitative approach with a within-subject repeated measures experimental design was employed, involving 34 ninth-grade students from a junior high school in Indonesia. Each participant completed mathematical problem-solving tasks under three controlled conditions: fast-tempo instrumental music (120–190 BPM), slow-tempo instrumental music (60–80 BPM), and silence. Cognitive performance was measured using accuracy scores, and subjective cognitive load was assessed through the NASA-TLX. Data were analyzed using Repeated Measures ANOVA and validated with the Friedman test due to partial violations of normality assumptions. The results indicated that the fast-tempo condition produced the highest mean accuracy, followed by no music and slow-tempo music. However, the differences were not statistically significant, although a moderate effect size suggested practical relevance. Pairwise comparisons revealed a consistent trend favoring fast-tempo music over slow-tempo and no-music conditions. Notably, NASA-TLX scores indicated that the fast-tempo condition produced significantly lower perceived cognitive load (M = 50.07) compared to slow-tempo (M = 61.59), χ²(2) = 13.41, p = .001, suggesting that fast-tempo music reduced subjective mental effort even when accuracy gains were not statistically significant. These findings support the theoretical perspectives of Cognitive Load Theory and arousal-mood theory, indicating that optimal levels of auditory stimulation may enhance cognitive processing efficiency. The results highlight the practical relevance of fast-tempo music in academic settings and underscore the need for further research with larger samples and physiological measures.
Machine Learning-Based Early Warning for Student Dropout: Evidence from LMS Behavioral Engagement Patterns in Online Higher Education Rhezwan Dhaifullah Romdhoni; Nuur Wachid Abdul Majid; Anggita Fitri Permatasari
Journal of Informatics and Vocational Education Vol. 9 No. 2 (2026): Journal of Informatics and Vocational Education - July
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i2.3508

Abstract

Student dropout in online higher education remains critically high, far exceeding face-to-face rates, yet declining behavioral activity in Learning Management Systems (LMS) offers key signals for early intervention. To identify robust predictors and model suitability for Early Warning Systems (EWS), this study presents a comparative analysis of machine learning for dropout prediction using clickstream data from the Open University Learning Analytics Dataset (OULAD), covering 32,593 students across seven undergraduate modules. Three supervised algorithms with Logistic Regression, Random Forest, and Support Vector Machine (SVM), were trained on 13 engineered features combining behavioral and demographic attributes from the Virtual Learning Environment (VLE), with Recall prioritized to minimize missed at-risk students. Results demonstrate that all models achieved strong discriminatory performance with AUC-ROC > 0.93; specifically, SVM provided the highest EWS fit with recall of 0.903, missing only 196 of 2,031 withdrawals (9.7%), while Random Forest attained the best overall accuracy (0.866) and AUC-ROC (0.940). Feature importance analysis further revealed that VLE behavior accounted for 85.0% of predictive power, with Activity Span emerging as the dominant predictor at 41.3%. Cross-module validation confirmed temporal engagement consistency as a robust, generalizable dropout signal. Therefore, these findings provide practical guidance for implementing data-driven EWS in online learning by prioritizing behavioral span metrics over static demographics
Development of Interactive E-Modules Based on Blender 3D Visualization: Analysis of Extraneous Cognitive Load and Its Effectiveness on Learning Outcome Mujahidin Abdillah Ashiddiqi; Rizki Hikmawan; Rhezwan Dhaifullah Romdhoni
Journal of Informatics and Vocational Education Vol. 9 No. 2 (2026): Journal of Informatics and Vocational Education - July
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i2.3519

Abstract

The growing demand for technology-based learning media has encouraged the development of interactive e-modules with three-dimensional visualization. However, poorly designed 3D media may increase extraneous cognitive load (ECL) and hinder students’ understanding. This study offers novelty by developing a 3D Blender-based interactive e-module designed with cognitive load management principles, particularly to minimize ECL while improving learning outcomes. The study aimed to develop the e-module and evaluate its impact on students’ cognitive load and achievement. This research used the Research and Development (R&D) method with the ADDIE model. The implementation involved 48 students at the University of Education Indonesia selected through purposive sampling using a one-group pretest-posttest design. Cognitive load was measured using a nine-item psychological scale questionnaire covering ICL, ECL, and GCL, while learning outcomes were assessed using a 15-item multiple-choice test. Data were analyzed descriptively and inferentially using the Shapiro-Wilk normality test and the Wilcoxon Signed Rank Test. The results showed that students’ ECL was low (x̄ = 2.09), indicating that the e-module did not overload working memory. Learning outcomes increased significantly from 34.67% to 84.67% (p = 0.000002). These findings indicate that the proposed e-module effectively supports conceptual understanding while reducing unnecessary cognitive load.
Immersive TOEFL Preparation in the Metaverse: Usability and Navigability of a Roblox-Based Game Developed via GDLC Rahmawati Salsabila; Rizki Hikmawan; Rhezwan Dhaifullah Romdhoni
Journal of Informatics and Vocational Education Vol. 9 No. 2 (2026): Journal of Informatics and Vocational Education - July
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i2.3520

Abstract

Despite the growing adoption of game-based learning in language education, three-dimensional metaverse platforms for TOEFL preparation remain critically underexplored, leaving learners reliant on drill-based 2D media that lack immersion and sustained engagement. This study addresses that gap by developing a Roblox-based TOEFL learning game using the Game Development Life Cycle (GDLC) and evaluating user understanding of its game flow through graduated formative evaluation. An R&D design was employed, implementing six GDLC stages alongside Tessmer's formative evaluation: one-on-one (n=3), small group (n=5), and field testing (n=40). Data were gathered via observation, interview, and Likert-scale questionnaires, and analyzed descriptively. The game was realized as an area-based environment with three thematic zones and a multi-level navigation system. Evaluations showed progressive quality improvement: one-on-one (M=3.25), small group (M=4.00), and field testing (M=3.84, 96.3% positive response), indicating satisfactory usability and navigability. However, awareness of the Challenge Room and return-route comprehension remain areas requiring refinement. Theoretically, this study demonstrates that GDLC paired with formative evaluation provides a structured and iterative framework for validating metaverse-based educational games. Practically, the findings offer actionable design principles for educators and developers building immersive, game-based language learning environments within social 3D platforms
Co-Authors Anggita Fitri Permatasari Hikmawan, Rizki Muhammad Rafly Juliawan Fernandes Mujahidin Abdillah Ashiddiqi Nuur Wachid Abdul Majid Rahmawati Salsabila