PENGELASAN SOALAN PEPERIKSAAN BERLANDASKAN TAKSONOMI BLOOM MENGGUNAKAN PEMBELAJARAN MESIN

Nazlia Omar, Yeo Yong Sheng

Abstract


Peperiksaan adalah perlu untuk menguji tahap kefahaman atas sesuatu perkara atau bidang. Untuk menjana soalan peperiksaan yang berkesan, pengajar haruslah mempunyai garis panduan untuk membina soalan yang seimbang daripada tahap kognitif yang berlainan yang dapat menilai pelajar dengan efektif. Garis panduan Taksonomi Bloom adalah antara garis panduan popular yang digunapakai oleh pengajar pada hari ini. Walau bagaimanapun, pengklasifikasian secara manual berdasarkan Taksonomi Bloom adalah satu perkara yang amat mencabar dan memerlukan masa yang panjang. Justeru, kajian ini mencadangkan satu model pengklasifikasian soalan peperiksaan berlandaskan taksonomi Bloom menggunakan teknik pembelajaran mesin. Pengelas yang digunakan dalam kajian ini ialah Mesin Vektor Sokongan(MVS), Bayes Naif (BN), Hutan Rawak (Random Forest), dan Jiran K-Terdekat (JKT). Untuk mendapatkan ketepatan yang lebih tinggi, set soalan data perlu dilakukan pra-pemprosesan seterusnya ciri pengekstrakan seperti beg perkataan digunakan.  Satu laman sesawang yang mesra pengguna dibangunkan bagi memudahkan pengajar untuk mengklasifikasikan soalan peperiksaan mereka dengan mudah dan cepat serta melihat hasil analisis dari pengelas. Prototaip dari kajian ini dapat membantu para pengajar untuk menganalisis soalan peperiksaan bagi memenuhi keperluan untuk tahap kognitif yang berbeza bagi pelajar mengikut tahap pengajian.


Keywords


Pembelajaran mesin; pengelasan soalan; taksonomi bloom

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References


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