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Self-explanations could increase student’s comprehension in complex domains; however, it works most efficiently with a human tutor who could provide corrections and scaffolding. In this paper, we present our attempt to scale up the use of self-explanations in learning programming by delegating assessment and scaffolding of explanations to an intelligent tutor. To assess our approach, we performed a randomized control trial experiment that measured the impact of automatic assessment and scaffolding of self-explanations on code comprehension and learning. The study results indicate that low-prior knowledge students in the experimental condition learn more compared to high-prior knowledge in the same condition but such difference is not observed in a similar grouping of students based on prior knowledge in the control condition. 

Domain modeling is a central component in education technologies as it represents the target domain students are supposed to train on and eventually master. Automatically generating domain models can lead to substantial cost and scalability benefits. Automatically extracting key concepts or knowledge components from, for instance, textbooks can enable the development of automatic or semi-automatic processes for creating domain models. We explore in this work the use of transformer based pre-trained models for the task of keyphrase extraction. Specifically, we investigate and evaluate four different variants of BERT, a pre-trained transformer based architecture, that vary in terms of training data, training objective, or training strategy to extract knowledge components from textbooks for the domain of intro-to-programming. We report results obtained using the following BERT-based models: BERT, CodeBERT, SciBERT and RoBERTa.