Aspect-Based Emotion Analysis on Speech for Predicting Performance in Collaborative Learning

This full research paper focuses on a natural language processing (NLP) driven approach to extract emotions from the speech in collaborative learning environments and analyze how they correlate with the learner's performance. Social competency is one of the base competencies that have been the target of many educational researchers in engineering and computing education during the past several years. Studies show that the low level of individual's performance is not just due to lack of intellectual or cognitive competencies but also lack of social skills impact performance in both educational and industrial domain. For this reason, Engineering Education is encapsulating social skills into the curriculum to prepare students for the fourth industrial revolution (i.e., Industry 4.0). Towards this goal in earlier work [1], we proposed a model to identify the correlation between the sentiments extracted from students' speech in teams and their performance. The results of polarity sentiment analysis showed a strong positive correlation between students' positive feelings in teams and their individual performance in the course. This study takes a further step and conducts multi-class emotion analysis on students' speech in teams. The process consists of two steps:1) extracting different classes of sentiment such as joy, anger, anxiety, etc., and identifying their correlation with students' performance using collaborative speech in an introductory programming course (CS1), 2) Aspect-Based Emotion Analysis (ABEA). The approach we adopt is the supervised machine learning method and rule-based models on speech datasets. After pre-processing the text, we identify multi classes of sentiments. Aspect extraction is accomplished through the Part of Speech (POS) tagging, and patterns are extracted from the identified aspects. Finally, we use the combination of emotion classes and aspect patterns as feature vectors to train the K-Nearest Neighbor (KNN) algorithm to predict students' performance.