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The paper focuses on a new type of interactive learning content for SQL programming - worked examples of SQL code. While worked examples are popular in learning programming, their application for learning SQL is limited. Using a novel tool for presenting interactive worked examples, Database Query Analyzer (DBQA), we performed a large-scale randomized controlled study assessing the value of worked examples as a new type of practice content in a database course. 

Educational data mining research has demonstrated that the large volume of learning data collected by modern e-learning systems could be used to recognize student behavior patterns and group students into cohorts with similar behavior. However, few attempts have been done to connect and compare behavioral patterns with known dimensions of individual differences. To what extent learner behavior is defined by known individual differences? Which of them could be a better predictor of learner engagement and performance? Could we use behavior patterns to build a data-driven model of individual differences that could be more useful for predicting critical outcomes of the learning process than traditional models? Our paper attempts to answer these questions using a large volume of learner data collected in an online practice system. We apply a sequential pattern mining approach to build individual models of learner practice behavior and reveal latent student subgroups that exhibit considerably different practice behavior. Using these models we explored the connections between learner behavior and both, the incoming and outgoing parameters of the learning process. Among incoming parameters we examined traditionally collected individual differences such as self-esteem, gender, and knowledge monitoring skills. We also attempted to bridge the gap between cluster-based behavior pattern models and traditional scale-based models of individual differences by quantifying learner behavior on a latent data-driven scale. Our research shows that this data-driven model of individual differences performs significantly better than traditional models of individual differences in predicting important parameters of the learning process, such as performance and engagement. 

In this paper, we describe the integration of a step-by-step interactive trace table into an existing practice system for introductory Java programming. These autogenerated trace problems provide help and scaffolding for students who have trouble in solving traditional one-step code tracing problems, accommodating a wider variety of learners. Findings from classroom deployments suggest the scaffolding provided by the trace table is a plausible form of help, most notably increases in performance and persistence and lower task difficulty. Based on usage data, we propose future implications for an adaptive version of the interactive trace table based on learner modeling. 

Individual differences have been recognized as an important factor in the learning process. However, there are few successes in using known dimensions of individual differences in solving an important problem of predicting student performance and engagement in online learning. At the same time, learning analytics research has demonstrated that the large volume of learning data collected by modern e-learning systems could be used to recognize student behavior patterns and could be used to connect these patterns with measures of student performance. Our paper attempts to bridge these two research directions. By applying a sequence mining approach to a large volume of learner data collected by an online learning system, we build models of student learning behavior. However, instead of following modern work on behavior mining (i.e., using this behavior directly for performance prediction tasks), we attempt to follow traditional work on modeling individual differences in quantifying this behavior on a latent data-driven personality scale. Our research shows that this data-driven model of individual differences performs significantly better than several traditional models of individual differences in predicting important parameters of the learning process, such as success and engagement. 

This paper contributes to the research on explainable educational recommendations by investigating explainable recommendations in the context of personalized practice system for introductory Java programming. We present the design of two types of explanations to justify recommendation of next learning activity to practice. The value of these explainable recommendations was assessed in a semester-long classroom study. The paper analyses the observed impact of explainable recommendations on various aspects of student behavior and performance. 

Over the last 10 years, learning analytics have provided educators with both dashboards and tools to understand student behaviors within specific technological environments. However, there is a lack of work to support educators in making data-informed design decisions when designing a blended course and planning appropriate learning activities. In this paper, we introduce knowledge-based design analytics that uncover facets of the learning activities that are being created. A knowledge-based visualization is integrated into edCrumble, a (blended) learning design authoring tool. This new approach is explored in the context of a higher education programming course, where instructors 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 visualization. We studied the differences in terms of cognitive load, controllability, confidence and ease of choice, design outcomes, and user actions within the system to compare both conditions with the objective of evaluating the impact of using design analytics during the decision-making phase of course design. Our results indicate that the use of a knowledge-based visualization allows the teachers to reduce the cognitive load (especially in terms of mental demand) and that it facilitates the choice of the most appropriate activities without affecting the overall design time. In conclusion, the use of knowledge-based design analytics improves the overall learning design quality and helps teachers avoid committing design errors. 

The goal of this workshop is to bring together the existing community of researchers working on Infrastructure Design for Data-Intensive Research in Computer Science Education and a community of Learning at Scale researchers focused on Computer Science Education. While both communities share many similar goals and could greatly benefit from each other work, the interaction between the communities is small. We hope that the proposed workshop will be instrumental in bringing together like-minded researchers from different communities, establishing collaboration, and expanding the scope of infrastructure project to address critical scaling issues. 

We present the initial version of a “live catalog” of LTI enabled smart learning objects that instructors and educators are able to preview and test before deciding whether to integrate these tools in their own courses. The catalog is available on the public Instructure Canvas site and currently showcases LTI tools from multiple educational institutions.