The benefits of collaborative learning, although widely reported, lack the quantitative rigor and detailed insight into the dynamics of interactions within the group, while individual contributions and their impacts on group members and their collaborative work remain hidden behind joint group assessment. To bridge this gap we intend to address three important aspects of collaborative learning focused on quantitative evaluation and prediction of group performance. First, we use machine learning techniques to predict group performance based on the data of member interactions and thereby identify whether, and to what extent, the group's performance is driven by specific patterns of learning and interaction. Specifically, we explore the application of Extreme Learning Machine and Classification and Regression Trees to assess the predictability of group academic performance from live interaction data. Second, we propose a comparative model to unscramble individual student performances within the group. These performances are then used further in a generative mixture model of group grading as an explicit combination of isolated individual student grade expectations and compared against the actual group performances to define what we coined as collaboration synergy--directly measuring the improvements of collaborative learning. Finally the impact of group composition of gender and skills on learning performance and collaboration synergy is evaluated. The analysis indicates a high level of predictability of group performance based solely on the style and mechanics of collaboration and quantitatively supports the claim that heterogeneous groups with the diversity of skills and genders benefit more from collaborative learning than homogeneous groups.