Affective states, a dimension of attitude, have a critical role in the learning process. In the educational setting, affective states are commonly captured by self-report tools or based on sentiment analysis on asynchronous textual chats, discussions, or students' journals. Drawbacks of such tools include: distracting the learning process, demanding time and commitment from students to provide answers, and lack of emotional self-awareness which reduces the reliability. Research suggests speech is one of the most reliable modalities to capture emotion and affective states in real-time since it captures sentiments directly. This research, which is an extension of the work originally presented in FIE conference'20 [1], analyses students' emotions during teamwork and explores the correlation of emotional states with students' overall performance. The novelty of this research is using speech as the source of emotion mining in a learning context. We record students' conversations as they work in low-stake teams in an introductory programming course (CS1) taught in active learning format and apply natural language processing algorithms on the speech transcription to extract different emotions from conversations. The result of our data analysis shows a strong positive correlation between students' positive emotions as they work in teams and their overall performance in the course. We conduct aspect-based sentiment analysis to explore the themes of the positive emotions and conclude that the student's positive feelings were mostly centered around course-related topics. The result of this analysis contributes to future development of predictive models to identify lowperforming students based on the emotions they express in teams at earlier stages of the semester in order to provide timely feedback or pedagogical interventions to improve their learning experience.