Complex skill mastery requires not only acquiring individual basic component skills, but also practicing integrating such basic skills. However, traditional approaches to knowledge modeling, such as Bayesian knowledge tracing, only trace knowledge of each decomposed basic component skill. This risks early assertion of mastery or ine ective remediation failing to address skill integration. We introduce a novel integration-level approach to model learners' knowledge and provide ne-grained diagnosis: a Bayesian network based on a new kind of knowledge graph with progressive integration skills. We assess the value of such a model from multifaceted aspects: performance prediction, parameter plausibility, expected instructional e ectiveness, and real-world recommendation helpfulness. Our experiments based on a Java programming tutor show that proposed model signi cantly improves two popular multipleskill knowledge tracing models on all these four aspects.

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Although many efforts are being made to provide educators with dashboards and tools to understand student behaviors within specific technological environments (learning analytics), there is a lack of work in supporting educators in making data-informed design decisions when designing a blended course and planning learning activities. In this paper, we introduce concept-level design analytics, a knowledge-based visualization, which uncovers facets of the learning activities that are being authored. The visualization is integrated into a (blended) learning design authoring tool, edCrumble. This new approach is explored in the context of a higher education programming course, where teaching assistants design labs and home practice sessions with online smart learning content on a weekly basis. We performed a within-subjects user study to compare the use of the design tool both with and without the visualization. We studied the differences in terms of cognitive load, design outcomes and user actions within the system to compare both conditions to the objective of evaluating the impact of using design analytics during the decision-making phase of course design.

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