Clustering Students for Group-Based Learning in Foreign Language Learning

Abstract

Big data make it possible to mine learning information for insights regarding student performance in foreign language learning (FLL). Group-based learning is a usual method to improve FLL, whose effectiveness is greatly influenced by student groups. The general grouping method is to divide students into groups by their teacher manually, which is not timely or accurate. To overcome the shortcomings of manual methods, this paper proposes an automatic grouping method based on clustering technologies. First, the student profile is built to model the student's knowledge level, which can be updated based on the results of examinations automatically. Then, to meet the different teaching goals, two student clustering methods are proposed: similarity student clustering and complementation student clustering. At last, the proposed methods are evaluated by comparing the students of clustered groups with those of the manual groups in the learning effectiveness. The experimental results show that the proposed methods are flexible, comprehensive, and timely compared with manual group methods.

Using K-Means to Determine Learner Typologies for Project-based Learning: A Case Study of the University of Education, Winneba

“…The results showed that 73% of the learners were satisfied with their groups because they received one of their top three priority choices for their preferred topic. [12] in their research proposed a grouping method based on two clustering techniques: similarity student clustering and complementation student clustering to automatically group students in Foreign Language Learning (FLL) for effective group based learning.…”

Sistem Pembagian Kelas Kuliah Mahasiswa Dengan Metode K-Means Dan K-Nearest Neighbors Untuk Meningkatkan Kualitas Pembelajaran

“…Permasalahan yang umum terjadi saat pembentukan atau pembagian kelas mahasiswa adalah perbedaan kemampuan yang dimiliki oleh mahasiswa di setiap kelasnya. Pengelompokan mahasiswa dengan kemampuan yang sama merupakan hal yang sangat penting dalam rangka meningkatan kualitas proses belajar mengajar [1][2][3]. Dengan pengelompokan mahasiswa di kelas yang sesuai, P mereka akan dapat saling membantu dalam proses pembelajaran [3].…”

“…Penelitian lain juga dilakukan [1] yang berjudul "Clustering Student for Group Based Learning in Foreign Language Learning". Penelitian yang dilakukan berfokus pada mekanisme pembentukan kelompok siswa otomatis dengan memanfaatkan metode clustering untuk pembelajaran bahasa asing.…”

Student Academic Mark Clustering Analysis and Usability Scoring on Dashboard Development Using K-Means Algorithm and System Usability Scale

“…The grouping results can later generate recommendations for superior classes in the analysis of each level as well as recommendations in the form of student placement by putting them into several groups and seat placement in the analysis of each study group. Grouping students according to their respective abilities is one factor that can improve the quality of teaching and learning [4]. Previous research used student performance data as a grouping model based on details of international students.…”

“…There have been many previous studies regarding student clustering using the K-Means algorithm. Li et al [4] looked for groups of students for group-based learning in foreign language lessons. Sya'iyah et al also present a clustering result on private educational institutions using K-Means Algorithm [5].…”