Muhammad Rafi Shidiq, - (2024) IMPLEMENTASI BAYESIAN KNOWLEDGE TRACING DALAM APLIKASI PEMBELAJARAN TOEFL BERBASIS GAMIFIKASI DENGAN OCTALYSIS FRAMEWORK. S1 thesis, Universitas Pendidikan Indonesia.
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Abstract
Penguasaan bahasa Inggris terutama dalam mencapai skor TOEFL yang tinggi menjadi syarat utama untuk mendapatkan berbagai peluang dalam melanjutkan pendidikan dan karier pada kancah internasional. Namun, banyak individu menghadapi kesulitan dalam mengerjakan tes TOEFL dan kekurangan motivasi untuk mempelajarinya, khususnya pada bagian Structure and Written Expression yang memerlukan analisis mendalam serta latihan rutin dalam mempersiapkannya. Untuk mengatasi permasalahan tersebut, penelitian ini mengembangkan aplikasi pembelajaran Structure and Written Expression TOEFL berbasis gamifikasi menggunakan Octalysis Framework untuk meningkatkan motivasi belajar serta mengintegrasikan algoritma Bayesian Knowledge Tracing sebagai dukungan teknologi pembelajaran adaptif untuk menyesuaikan preferensi belajar dan membantu pengguna dalam menghadapi kesulitan tes. Metode penelitian yang digunakan adalah Research and Development (R&D) dengan model ADDIE. Dimulai dengan menganalisis kebutuhan pengguna berdasarkan Framework Octaysis, mendesain kebutuhan sistem, mengembangkan aplikasi beserta implementasi Bayesian Knowledge Tracing, melakukan implementasi pada end-User, dan melakukan evaluasi. Hasil penelitian menunjukkan bahwa aplikasi berhasil dirancang dengan hasil pengujian tingkat kegunaan yang baik yaitu 84,93421 menggunakan System Usability Scale (SUS) yang mana tergolong “acceptable”. Hasil analisis Octalysis menunjukkan bahwa elemen gamifikasi berhasil diterapkan untuk meningkatkan motivasi User dengan rata-rata skor di atas 80 yang tergolong sangat baik untuk kedelapan core drives. Lebih lanjut, algoritma Bayesian Knowledge Tracing menunjukkan adanya peningkatan knowledge User secara bertahap. Mastery of the English language, particularly achieving a high TOEFL score is a crucial requirement for accessing various opportunities in advancing education and career at the international level. However, many individuals face challenges in taking the TOEFL test and lack the motivation to study for it, especially in the Structure and Written Expression section which requires deep analysis and regular practice. To address these issues, this study develops a TOEFL Structure and Written Expression learning application based on gamification using the Octalysis Framework to enhance learning motivation. The application also integrates the Bayesian Knowledge Tracing algorithm as adaptive learning technology support to tailor learning preferences and assist users in overcoming test difficulties. The research method employed is Research and Development (R&D) with the ADDIE model. The process begins with analyzing user needs based on the Octalysis Framework, designing system requirements, developing the application alongside the implementation of Bayesian Knowledge Tracing, implementing it with end-users, and conducting evaluations. The results show that the application was successfully designed with a good usability test score of 84.93, categorized as “acceptable” using the System Usability Scale (SUS). The Octalysis analysis reveals that the gamification elements were successfully applied to enhance user motivation, with an average score above 80, which is considered very good across all eight core drives. Furthermore, the Bayesian Knowledge Tracing algorithm demonstrates a gradual increase in user knowledge.
| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | https://scholar.google.com/citations?view_op=list_works&hl=en&user=7d-m9BQAAAAJ ID SINTA Dosen Pembimbing: Munir: 5974517 Rasim: 5990962 |
| Uncontrolled Keywords: | Bayesian Knowledge Tracing, Gamifikasi, Octalysis Framework, Structure and Written Expression, TOEFL Bayesian Knowledge Tracing, Education, Gamifikasi, Octalysis Framework, Structure and Written Expression, TOEFL |
| Subjects: | L Education > L Education (General) Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Program Studi Ilmu Komputer |
| Depositing User: | Muhammad Rafi Shidiq |
| Date Deposited: | 13 Sep 2024 07:58 |
| Last Modified: | 13 Sep 2024 07:58 |
| URI: | http://repository.upi.edu/id/eprint/124309 |
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