Adaptive support can help learners in Open-Ended Learning Environments (OELEs), where the free-form nature of the interaction can be confusing to students. In this paper, we design and evaluate an Intelligent Pedagogical Agent (IPA) for an OELE designed to foster Computational Thinking (CT). Specifically, we design help interventions for an in-the-wild scenario where students interact with the OELE in an unmonitored, self-directed manner. We build a student model by extracting meaningful student behaviors on real-world interaction data obtained during interaction in online classrooms and including expert insights. We show that these student models perform better than a baseline and have the potential for adaptive support in self-directed interaction with the OELE. We design an IPA with the help of teachers, leveraging the student behaviors extracted from data. Lastly, we get insights into the value of these help interventions by empirically evaluating the IPA in a formal user study.

Learning can be improved by providing personalized feedback adapting to the emotions that the learner may be experiencing. There is initial evidence that co-occurring emotions can be predicted during learning in Intelligent Tutoring Systems (ITS) through eye-tracking and interaction data. Predicting co-occurring emotions is a complex task and merging datasets has the potential to improve predictive performance. In this paper, we combine data from two user studies with an ITS, and analyze whether there is an improvement in predictive performance of co-occurring emotions, despite the user studies using different eye-trackers. In the pursuit towards developing real affect-aware ITS, we look at whether we can isolate classifiers that perform better than a baseline. In this regard we perform a series of statistical analyses and test out the predictive performance of standard machine learning models as well as an ensemble classifier for the task of predicting co-occurring emotions.