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All Journal Jurnal Ilmu Pendidikan International Journal of Engineering, Science and Information Technology
Singh, Vrijraj
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Retrieval Augmented Generation-Based Chatbot for Prospective and Current University Students Hartono, Luluk Setiawati; Setiawan, Esther Irawati; Singh, Vrijraj
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.951

Abstract

Universities utilize chatbots as assistants for users, especially prospective and current students, to access information and answer questions with relevant answers. This study introduces a new approach to an open-source model-based QA system using Gemma2-2b-it by combining Retrieval Augmented Generation (RAG) and Fine-tuning (FT) techniques. Previously, some studies have focused on only one approach, but this study will combine and compare both methods separately. Raw conversation data from WhatsApp, the main university website, and university PDF documents are used. The Retrieval Augmented Generation Assessment (RAGAS) framework will be used to evaluate the performance of the RAG model. In contrast, precision, recall, and similarity are used to assess the comparative performance of RAG and fine-tuning. The results of the RAGAS show that RAG using the base model is better than RAG using a fine-tuned model, which has 0.78 faithfulness, 0.64 answer relevancy, 0.81 context precision, and 0.68 context recall, so the overall RAGAS Score is 0.72. The comparison of precision and recall of fine-tuning are higher than those of using RAG, but the similarity score is not much different. Furthermore, the potential improvement for RAG of this study can be increased by adding a reranking process in the retrieved context, and fine-tuning of the embedding model can also be added to increase the retrieval process's performance. In addition, further experiments on various datasets and the challenge of overfitting in fine-tuning must be overcome so that the model can also perform better generalization.
Evaluating Student Learning Outcomes in Virtual Reality Adaptive Chemistry Machfudin, Mohammad Farid; Setiawan, Esther Irawati; Halim, Kevin Jonathan; Santoso, Joan; Utama, Agung Bella Putra; Gunawan, Gunawan; Kusuma, Samuel Budi Wardana; Singh, Vrijraj; Tuan Vu, Tong Nam
Jurnal Ilmu Pendidikan Vol 31, No 2 (2025): December
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um048v31i2p%p

Abstract

This study evaluates the effectiveness of a Virtual Reality (VR)–based adaptive learning application in enhancing high school students’ understanding of chemical compounds. The primary objective was to quantitatively assess the impact of the VR intervention on student learning outcomes across two distinct cohorts (N = 78). A pretest–posttest control-group design was employed, with two parallel groups (Group A and Group B) to ensure internal validity and comparability of results. The findings consistently indicate a marked contrast between the experimental and control conditions. Students in the control groups showed declines in performance, with negative learning gains of −8.32 and −15.20, suggesting learning loss when conventional instructional methods were used. In contrast, students exposed to the VR-based adaptive learning application demonstrated positive learning gains of +2.90 and +9.70, reflecting meaningful improvements in conceptual understanding. Further analysis of the intervention’s impact revealed effect sizes ranging from medium (Cohen’s d = 0.722) to very large (d = 2.182). These results indicate not only statistical significance but also substantial practical significance. Overall, the findings provide strong empirical evidence that the VR-based adaptive learning application is effective in preventing learning loss and significantly enhancing students’ understanding of chemical compounds when compared to traditional instructional approaches.
Co-Authors Esther Irawati Setiawan Gunawan Gunawan Halim, Kevin Jonathan Hartono, Luluk Setiawati Joan Santoso Machfudin, Mohammad Farid Samuel Budi Wardana Kusuma, Samuel Budi Wardana Tuan Vu, Tong Nam Utama, Agung Bella Putra