التفاصيل البيبلوغرافية
| العنوان: |
Agen Cerdas Untuk Perilaku Reward Appreciative Learning Dalam Game Pendidikan Kewirausahaan. (Indonesian) |
| Alternate Title: |
Intelligent Agent for Appreciative Learning Reward Behaviour in Entrepreneurship Education Game. (English) |
| المؤلفون: |
Haryanto, Hanny, Kardianawati, Acun, Rosyidah, Umi |
| المصدر: |
Techno.com; aug2017, Vol. 16 Issue 3, p325-336, 12p |
| Abstract (English): |
In entrepreneurship education games that focus on providing practical and cognitive experience, personalized rewards for in-game activities are an important element to strengthen the delivery of learning materials. However, the design of rewards that exist in educational games often do not have a good concept that impacted to a random and monotonous reward system. This study uses rewards compiled with the Appreciative Learning concept, which focuses on positive things such as peak achievement, opportunities, potential exploration and future optimism. The reward activity with the concept consists of four stages, namely Discovery, Dream, Design and Destiny. Personalized rewards are done using intelligent agent that setting up reward behavior which run in all four stages. Intelligent agent works in discrete, deterministic and static game environments. The intelligent agent will have knowledge of the environmental model and its condition. Intelligent agent behavior is modeled using Finite State Machine. This research resulted in a dynamic reward behavior in an entrepreneurship educational game environment designed with Appreciative Learning concepts. [ABSTRACT FROM AUTHOR] |
| Abstract (Indonesian): |
Dalam game pendidikan kewirausahaan yang berfokus pada pemberian pengalaman praktis dan kognitif, personalisasi reward untuk aktivitas dalam game adalah salah satu elemen penting untuk memperkuat penyampaian materi pembelajaran. Namun, perancangan reward yang ada dalam game pendidikan seringkali tidak memiliki konsep yang baik sehingga menghasilkan suatu sistem reward yang acak dan monoton. Penelitian ini menggunakan reward yang disusun dengan konsep Appreciative Learning, yang berfokus pada hal-hal positif seperti puncak pencapaian, peluang, eksplorasi potensi dan optimisme masa depan. Aktivitas reward dengan konsep tersebut terdiri dari empat tahap, yaitu Discovery, Dream, Design dan Destiny. Personalisasi reward dilakukan dengan pengaturan perilaku reward oleh agen cerdas yang berjalan dalam keempat tahap tersebut. Agen cerdas bekerja dalam lingkungan game yang diskrit, deterministik dan statis. Agen cerdas akan mempunyai pengetahuan mengenai model lingkungan dan kondisinya. Perilaku agen cerdas dimodelkan menggunakan Finite State Machine. Penelitian ini menghasilkan perilaku reward dinamis pada lingkungan game pendidikan kewirausahaan yang dirancang dengan konsep Appreciative Learning. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
