Intelligent Tutoring Systems (ITS) are required to intervene in a learning activity while it is unfolding, to support the learner. To do so, they often rely on performance of a learner, as an approximation for engagement in the learning process. However, in learning tasks that are exploratory by design, such as constructivist learning activities, performance in the task can be misleading and may not always hint at an en- gagement that is conducive to learning. Using the data from a robot mediated collaborative learning task in an out-of-lab setting, tested with around 70 children, we show that data- driven clustering approaches, applied on behavioral features including interaction with the activity, speech, emotional and gaze patterns, not only are capable of discriminating between high and low learners, but can do so better than classical approaches that rely on performance alone. First experiments reveal the existence of at least two distinct multi- modal behavioral patterns that are indicative of high learning in constructivist, collaborative activities.