This study investigated the individual patterns in the pairs as well as pair patterns in the context of pair program tracing and debugging to determine what characterizes collaboration and how these patterns relate to success, where success is measured in terms of performance task scores. Eye tracking methodologies and cross-recurrence quantification analysis were used to analyze these patterns. Machine learning techniques were employed to create models capable of predicting individual success in the pairs and pair success.Findings suggest that a successful collaboration is characterized as having individuals in the pairs who are always on-task, acquaint themselves first with the program, strike a good balance of encoding information and searching for errors, and who can quickly associate with program elements. A pair has better chances of succeeding when the individuals being paired together are highly proficient who frequently converge and engage in a productive conversation. It is possible to create a model capable of predicting individual success in the pairs and a model that is able to predict pair success
