Developing a Computer-Mediated Communication Competence Predicting Model Based on Learning Behavior Features

This study aims to develop a computer-mediated communication (CMC) competence forecasting model based on several considered well-known machine learning schemes and learning behavior features collected by a micro-behavior recorder from the learners while using a web-based collaborative problem-based learning (CPBL) system to perform a problem-solving learning activity. To summarize the big data generated from a huge amount of micro behaviors into the useful behavior features for constructing a good CMC competence forecasting model, this study developed the learning micro-behavior classification structure according to the collected data features and the concept of CMC. An effective method for constructing a high correctness and stableness CMC competence forecasting model was proposed and examined. Besides, the effects of learning situations on the accuracy of the CMC competence forecasting model were also discussed.