A learning style classification approach based on deep belief network for large-scale online education

Abstract

With the rapidly growing demand for large-scale online education and the advent of big data, numerous research works have been performed to enhance learning quality in e-learning environments. Among these studies, adaptive learning has become an increasingly important issue. The traditional classification approaches analyze only the surface characteristics of students but fail to classify students accurately in terms of deep learning features. Meanwhile, these approaches are unable to analyze these high-dimensional learning behaviors in massive amounts of data. Hence, we propose a learning style classification approach based on the deep belief network (DBN) for large-scale online education to identify students’ learning styles and classify them. The first step is to build a learning style model and identify indicators of learning style based on the experiences of experts; then, relate the indicators to the different learning styles. We improve the DBN model and identify a student’s learning style by analyzing each individual’s learning style features using the improved DBN. Finally, we verify the DBN result by conducting practical experiments on an actual educational dataset. The various learning styles are determined by soliciting questionnaires from students based on the ILS theory by Felder and Soloman (1996) and the Readiness for Education At a Distance Indicator. Then, we utilized those data to train our DBNLS model. The experimental results indicate that the proposed DBNLS method has better accuracy than do the traditional approaches.

Teaching and Learning Styles on Moodle: An Analysis of the Effectiveness of Using STEM and Non-STEM Qualifications from a Gender Perspective

“…It must all be supported through necessary innovation in the form of teaching that implies the use of technological resources [40]. It implies facilitating teaching staff with different instructional methods in virtual environments, so that they use the method that is best suited to their teaching styles and the learning style of their students, to achieve effective and personalized teaching [41,42].…”

Profil Gaya Belajar Mahasiswa Prodi Pendidikan Matematika Berdasarkan Model Vark

“…Gaya belajar mempengaruhi prestasi belajar secara signifikan (Weggelaar-Jansen et al, 2015); (Wassahua, 2016); (El-Bishouty et al, 2019); (Habibah et al, 2019); kepercayaan diri (Marković & Jovanović, 2012), penalaran matematis (Marwiyah et al, 2020) dan meningkatkan pemahaman matematis (Azizah & Dien, 2017). Berdasarkan pengaruh positif gaya belajar terhadap hasil belajar maka banyak pakar membuat sistem dalam mengidentifikasi gaya belajar (Seyal & Rahman, 2015) (Prasetyo & Iqbal, 2016); (Zhang et al, 2020) sehingga dapat memprediksi gaya belajar yang tidak pasti (Deborah et al, 2015). Strategi belajar mahasiswa telah dieksplorasi berdasarkan konteks, kreativitas (Marković & Jovanović, 2012) teori belajar, studi empiris dan aspek pedagogis yang berinteraksi satu sama lain (Khanal et al, 2021).…”

“…Dalam lingkungan belajar di perguruan tinggi, perbedaan individu antara mahasiswa jelas terlihat dalam hal waktu, durasi pembelajaran, konten pembelajaran yang dipilih, dan pola interaksi (Zhang et al, 2020). Berdasarkan hasil observasi pada mahasiswa prodi Pendidikan Matematika Universitas Muhammadiyah Semarang beberapa mahasiswa lebih suka menyelesaikan tugas belajar di siang hari, sementara yang lain lebih suka belajar di malam hari.…”

“…Dengan alasan tersebut maka mahasiswa menggunakan lebih dari satu gaya belajar. Melalui kombinasi gaya belajar lebih dari satu akan meningkatkan hasil belajar (Barry & Egan, 2018); (Zhang et al, 2020); (Papadatou-Pastou et al, 2021). Penentuan kombinasi gaya belajar setiap mahasiswa berbeda-beda.…”

“…Kepuasan dalam pemilihan gaya belajar akan meningkatkan persepsi peserta didik yang dapat mendorong partisipasi mereka dalam proses pembelajaran (Marković & Jovanović, 2012). Ketika mahasiswa menggunakan gaya belajar yang bervariasi, akan meningkatkan hasil belajar (Barry & Egan, 2018); (Zhang et al, 2020); (Papadatou-Pastou et al, 2021).…”