Fahlevi, Achmad Reza (2025) Integrating big data concepts into education through rpg games: a study using agile methodologies / Achmad Reza Fahlevi</p>. Diploma thesis, Universitas Negeri Malang.
Full text not available from this repository.Abstract
p The incorporation of big data principles into educational programs is becoming increasingly vital in the context of Industry 4.0. Vocational high schools face considerable challenges in imparting these abstract concepts primarily due to insufficient interactive learning tools. This paper details the development and evaluation of Data Quest Big Data Adventure an educational game aimed at improving students comprehension of big data. Based on constructivist learning theory and gamification principles this game was developed utilizing agile methodologies and evaluated with 23 students at SMKN 10 Malang. Surveys and questionnaires were conducted post-gameplay sessions to assess students engagement and motivation levels. The tools gathered quantitative and qualitative data offering insights into the game s impact on students attitudes towards learning Big Data. The media validation results demonstrated a high potential effectiveness of 96% usability of 84% and satisfaction of 100%. Field tests indicated significant engagement evidenced by high scores in accomplishment (84.51%) challenge (86.43%) competition (88.24%) guidance (87.57%) and social experience (85.57%). Immersion and playfulness achieved scores of 81.43% and 84.57% respectively indicating the game s effectiveness in engaging student interest. Results regarding student motivation were favourable with scores recorded as follows attention (80.80%) relevance (80%) confidence (78.43%) and satisfaction (81.43%). The findings indicate that gamified educational tools such as Data Quest can substantially improve big data literacy and motivation in vocational students. The interactive design and competitive elements enhance engagement rendering abstract big data concepts more comprehensible and cultivating essential skills required for the data-driven needs of the industry 4.0 workforce. /p
| Item Type: | Thesis (Diploma) |
|---|---|
| Divisions: | Fakultas Teknik (FT) > Departemen Teknik Elektro (TE) > S1 Teknik Informatika |
| Depositing User: | library UM |
| Date Deposited: | 17 Feb 2025 04:29 |
| Last Modified: | 09 Sep 2025 03:00 |
| URI: | http://repository.um.ac.id/id/eprint/400158 |
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