KOMPARASI ALGORITMA K-MEAN DAN HIERARCHICAL UNTUK PENGELOMPOKAN PENGARUH COVID-19 TERHADAP PENDIDIKAN

Tujuan penelitian ini adalah untuk mengelompokan dampak covid-19 terhadap dunia pendidikan. Covid-19 adalah salah satu virus yang mempunyai dampak besar terhadap bidang politik, ekonomi, budaya, olahraga, pendidikan dan bidang-bidang lainnya. Dampaknya dalam  bidang pendidikan yaitu penutupan sekolah-sekolah, universitas, dan lembaga-lembaga kursus sehingga mengakibatkan kegiatan belajar dilakukan secara online dari rumah. Metode dalam penelitian ini dimulai dengan mengabil dataset yang bersumber dari dataset public https://www.kaggle.com/ untuk memperoleh data impact covid-19 terhadap pendidikan. Tahap selanjutnya adalah preprosesing data untuk memfilter attribut-atribut yang paling berpengaruh terhadap pendidikan menggunakan excel dan pemograman phyton, dataset yang yang telah melalui mining data dilanjutkan untuk membuat pola menggunakan algoritma  machine learning yaitu hierarchical clustering dan k-mean clustering algoritma pengelompokan digunakan. Clustering adalah proses pengelompokan objek yang mirip menjadi kelompok yang berbeda atau pembagian kumpulan data menjadi subset berdasarkan pengukuran jarak. Hasil yang diharapkan dari penelitian ini adalah hasil perbandingan dari algoritma k-mean dan hierarchical clustering mana yang kelak mempunyai akurasi tertinggi dalam pengelompokan pengaruh covid-19 terhadap pendidikan. &nbsp

A Student Learning Style Auto-Detection Model in a Learning Management System

Learning style plays an important role in enabling students to have an efficient learning process. This paper proposes an auto-detection model of student learning styles in learning management systems based on student learning activities. A literature review was conducted to investigate the components of online learning activities. The search terms used were "online learning activities", "learning management systems", and "Felder-Silverman Learning Style Model (FSLSM)." A combination of the search terms above was also executed to enhance the search process. Based on the results of the review, eleven classes of online learning activities were identified, namely forum, chat, mail, reading materials, exam delivery time, exercises, access to examples, answer changes, learning materials, exam results, and information access. The online learning activities identified were then mapped to the Felder-Silverman model based on four model dimensions: processing, perception, input, and understanding. The proposed model shows the attributes of the online learning activities based on the dimensions in the FSLSM. The proposed model can assist educators to improve learning content according to the suitability of students and recommend appropriate learning materials to students based on their characteristics and preferences. Future studies include the use of machine learning algorithms such as decision trees to auto-detect student learning styles in learning management systems

Self-organizin map clustering method for the analysis of e-learning activities

Students‘ interactions with e-learning vary according to their behaviours which in turn, yield different effects to their academic performance. Some students participate in all online activities while some students participate partially based on their learning behaviours. It is therefore important for the lecturers to know the behaviours of their students. But this cannot be done manually due to the unstructured raw data in students‘ log file. Understanding individual student‘s learning behaviour is tedious. To solve the problem, data mining approach is required to extract valuable information from the huge raw data. This research investigated the performance of Self-organizing Map (SOM) to analyze students‘ elearning activities with the aim to identify clusters of students who use the e-learning environment in similar ways from the log files of their actions as input. A study on Meaningful Learning Characteristics and its significance on students‘ leaning behaviors were carried out using multiple regression analysis. Then SOM clustering technique was used to group the students into three clusters where each cluster contains students who interact with the E-learning in similar ways. Behaviors of students in each cluster were analyzed and their effects on their learning success were discovered. The analysis shows that students in Cluster1 have the highest number of interactions with the e-learning (Very Active), and having the highest final score mean of 91.12%. Students in Cluster2 have less number of interactions than that of Cluster1 and have final score mean of 75.65%. Finally, students Cluster3 have least number of interactions than the remaining clusters with final score means is 36.57%. The research shows that, students who participate more in Forum activities emerged the overall in learning success, while students with lowest records on interactions have lowest performance. The research can be used for early identification of low learners to improve their mode of interactions with e-learning.MUSA WAKIL BAR

Uma Revisão das Diferentes Abordagens Computacionais para Detecção de Estilos de Aprendizagem de Estudantes em Sistemas para Educação a Distância

Com a evolução da Educação a Distância nos últimos anos, muito se tem estudado sobre a importância de se considerar estilos de aprendizagem no processo de ensino a distância. No entanto, a identificação dos estilos de aprendizagem de um estudante em um ambiente EaD não é uma tarefa trivial. Este artigo busca realizar uma revisão sobre as diferentes abordagens computacionais para detecção de estilos de aprendizagem presentes na literatura. As abordagens computacionais aqui apresentadas são baseadas em técnicas da Inteligência Artificial capazes de realizar a detecção dos estilos de aprendizagem de forma automática a partir do comportamento do aluno em um ambiente virtual de aprendizagem. No total, foram selecionados 26 artigos, dos quais pode-se analisar 15 abordagens diferentes para detecção de estilos de aprendizagem. Dentre as abordagens, a mais utilizada nos trabalhos selecionados foram as Redes Bayesianas. Além disso, percebeu-se que as pesquisas relacionadas a detecção automática de estilos de aprendizagem, mesmo após as críticas recentes a teoria, continuam sendo desenvolvidas e aperfeiçoadas