鍾澤華

Over the past few decades, collaborative learning has been widely utilized in learning with favorable and positive results. With the advanced Internet technology, collaborative e-Learning has become a trend of learning. Collaborative learning involves forming “learning teams.” However, traditional learning team formation based on time-consuming personality tests seems not practical for collaborative eLearning. Figuring out a method to effectively identify team members’ personality traits for role assignments is a challenge in collaborative e-Learning. The purpose of this study is to develop an Automatic Personality Pattern Recognition Mechanism (APPR) where, through the analysis of each learner’s dialogue, certain type of personality (NEO-FFI) is identified for role assignments in forming a learning team. Firstly, a collaborative e-learning platform was developed in which personality types with corresponding dialogue transcripts were collected. Secondly, these dialogue transcripts were converted into three different types of parts-of-speech, with which the concept of collocation was used to find the difference in how these parts-of-speech were used by people of different personality types. By them, personality types were categorized. Thirdly, term frequency–inverse document frequency was used as a screening method for features extraction and personality types were classified through SVM (Support Vector Machine) as a training stage. Lastly, using this personality identification mechanism, collaborative e-learning teams were formed with each individual learner assigned a role in a respective group in accord with his/her personality type. Empirical results showed that proposed APPR can identify personality types with overall classification accuracy up to 86%.