Serious Game Berbasis Taksonomi Bloom: Sebuah Pendekatan Alternatif Penilaian Pembelajaran Matematika Berbantuan Teknologi Informasi

Sukajaya, I Nyoman (2016) Serious Game Berbasis Taksonomi Bloom: Sebuah Pendekatan Alternatif Penilaian Pembelajaran Matematika Berbantuan Teknologi Informasi. Doctoral thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pada penelitian ini dikembangkan sebuah pendekatan baru dalam penilaian pembelajaran matematika berbantuan serious game dengan melibatkan komponen pengetahuan geometri bangun datar jajar genjang dan komponen pedagogi yakni taksonomi belajar menurut Bloom. Pendekatan penilaian ini diusulkan sebagai alternatif baru merekam data pembelajar yang representatif mewakili karakteristik individu mereka. Serious game yang diimplementasikan dikembangkan mengikuti kerangka teknis serious game yang konstruktivis. Tantangan di serious game didistribusikan ke dalam tiga level domain kognitif Bloom yang dimplementasikan di SD: kemampuan mengingat (C1), memahami (C2), dan menerapkan (C3). Serious game juga mengatur level kesukaran tantangan secara dinamis berdasarkan pengalaman pemain pada tantangan sebelumnya. Pengaturan level kesukaran secara dinamis ditujukan agar pemain tidak cepat frustrasi atau bosan dalam permainan. Serious game yang diimplementasikan dalam penilaian sudah melalui uji penerimaan dan uji tanggapan dari pengguna. Klasifikasi data permainan dilakukan melalui metode Bayes Net (BN), Naïve Bayes (NB), dan J48. Dalam melakukan klasifikasi, penerapan tiga metode digabungkan dengan dua opsi tes: crossvalidation dan percentage split. Klasifikasi di masing-masing perlakukan dikerjakan dalam sepuluh ulangan melibatkan sub data yang dipilih acak sebagai data pengujian. Hasil pengujian menunjukkan: (1) dari delapan skenario pengujian penerimaan pengguna diperoleh bahwa keseluruhan masukan skenario pengujian memberikan luaran yang sesuai harapan; (2) rata-rata skor respon pengguna yang dikumpulkan menggunakan kuesioner skala Likert dengan lima opsi terletak dalam interval kategori respon positif (59,93); (3) persentase kebenaran klasifikasi tertinggi yang dihasilkan pada klasifikasi data permainan adalah 88,5% yang dihasilkan melalui penerapan metode J48 yang digabungkan dengan opsi tes percentage split = 85%. Kategori agreement pada penerapan metode J48 dengan opsi tes percentage split adalah Baik (78%). Dari tiga bentuk pengujian disimpulkan bahwa selayaknya metode J48 dipilih dalam melakukan klasifikasi data permainan serta penilaian melibatkan serious game berbasis taksonomi Bloom dijadikan alternatif dalam penilaian pembelajaran materi geometri bangun datar untuk siswa SD kelas 5. ==================================================================================== We developed a new approach for mathematics' learning assessment applying a serious game which is called BoTySeGa. This approach was proposed as an alternative way for recording learners' data which are representative to understand the characteristic of learners. The game implemented in assessment involves the three aspects of a serious game: game, knowledge, and pedagogy. We involve the plane geometry of parallelogram for the 5th elementary students and Bloom's taxonomy successively as knowledge and pedagogy aspects of the game. The serious game was developed following the serious game constructivist framework for children’s learning. Inside the game; the challenges are distributed into the first three of Bloom's cognitive domain which are implemented in elementary school: remember, understand, and apply. The game system adjusts dynamically challenge's level of difficulty in consideration with players' experience on the previous challenge. This approach is designed to bring players far away of boredom and frustration. The serious game applied in the proposed assessment has been tested through user acceptance testing and players' respond to the usage of the game in assessment. Gameplay data are classified through three methods i.e.: Bayes Net, Naïve Bayes, and J48. Each method is conducted with two testing options: crossvalidation and percentage split. The classification in each treatment is done in ten times of repetition. Test results show that: (1) user acceptance testing involving 85 learners shows that BoTySeGa has fulfilled the learning assessment requirement, (2) the average score of players' response recorded utilizing five-points Likerttype of questionnaire is 59,93, it falls within "Positive" category, (3) the highest percentage of correctly classification of gameplay data is 88,5% which is calculated through classification applying method J48 with percentage split testing option 85%. Level agreement of classification is 0.78 which is Good. Based on testing results, we suggest the use of J48 method for the classification of gameplay data and support the implementation of mathematics’ learning assessment utilizing Bloom's taxonomy-based serious game as an alternative assessment in learning.

Item Type: Thesis (Doctoral)
Additional Information: RDE 003.8 Suk s
Uncontrolled Keywords: serious game; taksonomi Bloom; Penilaian pembelajaran; Matematika; Bloom taxonomy; learning assessment; Mathematics
Subjects: Q Science > QA Mathematics > QA269 Game theory
Divisions: Faculty of Industrial Technology > Electrical Engineering > (S3) PhD Theses
Depositing User: Mrs Anis Wulandari
Date Deposited: 29 May 2017 04:20
Last Modified: 27 Dec 2018 07:48
URI: http://repository.its.ac.id/id/eprint/41399

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