Three years after the COVID-19 pandemic emerged, we have adapted to the new normal, especially in the education field. Learning with video conferences has become our daily activity, and learning tools have gotten more prominent attention to gain student engagement, especially in emergency remote teaching (ERT). Since the trends of metaverse campaigns by meta, augmented reality (AR) has increased recognition in education contexts. However, very little research about the acceptance of augmented reality in video conferences, especially among university students. This paper aims to measure acceptance of AR in video conferences to motivate and inspire students to gain benefits and get impactful technology in the learning process. The research gathered data from a survey of 170 university students (from 5 majors in the study program and 17 different demographic areas) using unified theory of acceptance of technology 2 (UTAUT2). The result reveals that variables significantly impact acceptance: performance expectancy, hedonic motivation, and habit. The least significant but still positive effects are effort expectancy, social influence, and facilitating conditions. The study will provide helpful information on AR technology in video conferences and help top-level management in the university that provides online/distance learning in the early diffusion stage for metaverse in education.
Clustering is a process to group data into several clusters or groups so the data in one cluster has a maximum level of similarity and data between clusters has a minimum similarity. X-means clustering is used to solving one of the main weaknesses of K-means clustering need for prior knowledge about the number of clusters (K). In this method, the actual value of K is estimated in a way that is not monitored and only based on the data set itself. The results of the study using the X-Means algorithm with the Davies-Bouldin Index evaluation to determine the number of Centroid clusters is done by modifying the X-Means method to do some centroid determination to get 11 iterations. The result is produces cluster members that have a good level of similarity with other data. In determining the number of centroids, use the Davies-Bouldin Index method where testing with 2 clusters has a minimum value with a DBI value close to 0.
hal ini penulis mengelompokan data siswa baru sekolah menengah kejuruan tahun ajaran 2014/2015. Pengelompokan tersebut berdasarkan kriteria -kriteria data siswa. Pada penelitian ini, penulis menerapkan algoritma K-Means Clustering untuk pengelompokan data siswa baru sekolah menengah kejuruan. Dalam hal ini, pada umumnya untuk memamasuki jurusan hanya disesuaikan dengan nilai siswa saja namun dalam penelitian ini pengelompokan disesuaikan kriteria -kriteria siswa seperti penghasilan orang tua, tanggungan anak orang tua dan nilai tes siswa. Penulis menggunakan beberapa kriteria tersebut agar pengelompokan yang dihasilkan menjadi lebih optimal. Tujuan dari pengelompokan ini adalah terbentuknya kelompok jurusan pada siswa yang menggunakan algoritma K-Means clustering. Hasil dari pengelompokan tersebut diperoleh tiga kelompok yaitu kelompok tidak lulus, kelompok rekayasa perangkat lunak dan kelompok teknik komputer jaringan. Terdapat pusat cluster dengan Cluster-1=1.4;2.2;2.2, Cluster-2= 2.28;1.64;4 dan Cluster-3=5;2;6. Pusat cluster tersebut didapat dari beberapa iterasi sehingga mengahasilakan pusat cluster yang optimal.
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