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Developing Intelligent Adaptive Virtual Class Model (IAVCM) System based on Tokenization


Jehad Hammad and Mochamad Hariadi, Mauridhi Hery Purnomo, Nidal Jabari


Vol. 18  No. 4  pp. 20-27


This study aimed to develop an Intelligent Adaptive Virtual Class Model (IAVCM) System. IAVCM based on Adaptive Virtual Class Model (AVCM) which was introduced by Jabari [1] in 2014. The aim of our study is to enhance the AVCM system to be Intelligent by using machine learning model. The main goal of our system is to convert the chat tool in the virtual class room from normal chat tool to educational one, by modeling the text and mining the context. IAVCM is a client/server system that works in two modes the training mode and the use mode. By the end of the chat session the learners will be evaluated using three ways including: chat context analysis, time spend in the chat and peers evaluation. The system consists of different components that offer the needed services for the experts to feed the system in order to make it intelligent one. The main services include course management, Language management, experiments management, Tokenization engine, Keywords Auto Leveling Algorithm (KALA), Students profiles, and chat management. In our system we develop a machine learning model to evaluate our system. Two dataset were used, one set to train the model and another that is unseen testing set used to predict the model consistency. The system support multi languages including English, Arabic and Indonesian. The system trained and evaluated by applying 60 chat sessions for 120 students for four different courses in Arabic language.


Adaptive E-learning, AVCM, Tokenize, IAVCM, Text modeling.