An Investigation on Students’ Level of Adjustment to Online Education with C5.0 Decision Tree Algorithm

Abstract

This study aims to determine the independent variables that have a significant effect on the level of students’ adaptation to online education and their order of importance. Relational screening model was used in the study. Adaptability Level in Online Education dataset provided by Kaggle repository constitutes the main data source for this study. C5.0 decision tree algorithm was used to analyze the data. It was found that that 51,867% of the students had a moderate level of adaptation to online education, while 39,834% had low, and 8,299% had a high level adaptation. The findings indicate that students’ level of adaption to online learning is insufficient. We also found that the variable that best explains the online education compliance levels is daily class duration. “Financial condition” was found as the best explanatory variable of the cluster formed by the students whom “daily class duration” was between “1-3 hours”. “Age” was found as the best explanatory variable of the cluster formed by the students whom “daily class duration” was between “1-3 hours” and has “financial condition” of “poor”, “mid” and “rich”.

Klasifikasi Tingkat Kemampuan Adaptasi Siswa dalam Pembelajaran Online Menggunakan Decision Tree

“…Perkembangan ilmu pengetahuan dan teknologi telah mendorong kita untuk menyesuaikan diri dengan penggunaan teknologi dalam berbagai bidang, termasuk komunikasi, pendidikan, dan informasi. Salah satu hal yang menonjol adalah pendidikan, di mana pembelajaran online telah mendapatkan popularitas yang signifikan di banyak lembaga pendidikan [1], [2]. Pembelajaran online adalah inovasi pendidikan yang melibatkan penggunaan teknologi informasi dalam proses belajar-mengajar.…”

“…Hark Söylemez N, dkk [2] mengevaluasi kinerja kursus pembelajaran online menggunakan algoritma Decision Tree berdasarkan umpan balik dan faktor kepuasan siswa, seperti konten kursus, instruktur, interaksi, penilaian, dan dukungan. Penelitian ini menggunakan dataset siswa dari sebuah universitas di Cina dan menerapkan algoritma C4.5 untuk membangun Decision Tree.…”