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All Journal Journal of Applied Data Sciences JPP (Jurnal Pendidikan dan Pembelajaran)
Syaad Patmanthara
State University of Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Comparison Learning Model AIR and TAI Combined With Cognitive Conflict Strategy Againts Active Learning and Concept Understanding Dodik Septian Ferdiansyah; Syaad Patmanthara; Ilham Ari Elbaith Zaeni
JPP (Jurnal Pendidikan dan Pembelajaran) Vol. 27 No. 1 (2020)
Publisher : Lembaga Pengembangan Pendidikan dan Pembelajaran, Universitas Negeri Malang in Collaboration with Asosiasi Pendidik dan Pengembang Pendidikan Indonesia (APPI)

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Abstract

There are several problems in the learning process of Basic Electricity and Electronics at SMK Negeri 8 Malang, including: (1) When learning takes place students do not pay attention and listen to the teacher when delivering material, (2) The teacher does not focus on learning activities to students, (3) Students are less active in asking and expressing his opinion about the material that has been taught. This study uses a variety of learning models and methods that can improve students' learning activeness and conceptual understanding, namely the Auditory, Intellectual, Repetition (AIR) learning model and the Team Assisted Individualization (TAI) learning model, each of which is combined with cognitive conflict strategies. The research design used a quasi experimental design with a non-equivalent control group design type. The data analysis technique consisted of normality test, homogeneity test, two mean similarity test, and hypothesis testing. The conclusion of this study is that the AIR learning model combined with cognitive conflict strategies is superior to the TAI learning model combined with cognitive conflict strategies
Clustering-Based Adaptive UX in E-Learning Systems: Aligning Microservices with the 4C Framework Poetri Lestari Lokapitasari Belluano; Syaad Patmanthara; Muhammad Ashar; Fachrul Kurniawan; Gulsun Kurubacak
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.884

Abstract

This study introduces a clustering-driven adaptive User Experience (UX) architecture for e-learning systems, aligning machine learning segmentation with the 21st-century 4C educational framework (critical thinking, communication, collaboration, creativity). The objective is to dynamically personalize digital learning interactions through a microservices architecture responsive to users' UX profiles. A quantitative survey was conducted involving 50 active users of Shopee and Tokopedia, whose interaction feedback was mapped using the User Experience Questionnaire (UEQ). Three unsupervised clustering techniques—KMeans, Agglomerative, and DBSCAN—were compared. KMeans outperformed the others with a silhouette score of 0.157, compared to 0.146 for Agglomerative and −0.017 for DBSCAN, identifying three meaningful clusters representing high, medium, and low UX proficiency. A one-way ANOVA test confirmed statistically significant differences (p 0.01) among the clusters in dimensions such as error clarity, support responsiveness, and user confidence. These UX profiles were then mapped to individualized microservices: Cluster 0 received autonomous content with minimal support, Cluster 1 was offered guided prompts, and Cluster 2 was provided with simplified interfaces and proactive assistance. Each cluster was aligned with specific 4C competencies to ensure pedagogical relevance. The proposed architecture, built with gRPC-based microservices, enabled asynchronous, low-latency personalization based on user cluster membership. The novelty of this research lies in its dual alignment—technological (microservices + machine learning) and educational (4C competency mapping)—to construct a scalable and responsive e-learning environment. The system design, although validated through simulation, demonstrates a practical foundation for future deployment in platforms like Moodle or OpenEdX. By linking behavioral UX clustering to pedagogical intervention strategies, this study offers a model for adaptive, data-informed instructional systems that are both scalable and learner-centered.
Co-Authors Ashar, Muhammad Dodik Septian Ferdiansyah Fachrul Kurniawan Gulsun Kurubacak Ilham Ari Elbaith Zaeni Poetri Lestari Lokapitasari Belluano