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We present the results of a study where we provided students with textual explanations for learning content recommendations along with adaptive navigational support, in the context of a personalized system for practicing Java programming. We evaluated how varying the modality of access (no access vs. on-mouseover vs. on-click) can influence how students interact with the learning platform and work with both recommended and non-recommended content. We found that the persistence of students when solving recommended coding problems is correlated with their learning gain and that specific student-engagement metrics can be supported by the design of adequate navigational support and access to recommendations' explanations. 

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. We report the results of the classroom study examining the usage and the impact of DBQA. Among other aspects, we explored the effect of textual step explanations provided by DBQA. 

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. 

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. 

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. 

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. 

Personalized recommendation of learning content is one of the most frequently cited benefits of personalized online learning. It is expected that with personalized content recommendation students will be able to build their own unique and optimal learning paths and to achieve course goals in the most optimal way. However, in many practical cases students search for learning content not to expand their knowledge, but to address problems encountered in the learning process, such as failures to solve a problem. In these cases, students could be better assisted by remedial recommendations focused on content that could help in resolving current problems. This paper presents a transparent and explainable interface for remedial recommendations in an online programming practice system. The interface was implemented to support SQL programming practice and evaluated in the context of a large database course. The paper summarizes the insights obtained from the study and discusses future work on remedial recommendations. 

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. 

Recommendations for online educational systems generally differ from recommendations generated in other contexts (e.g. movies, e-commerce), given that students’ level of knowledge rather then their interests is key for suggesting the most appropriate content. Thus, the challenge of making recommendations more transparent is closely tied to how student skills are estimated and conveyed. In this paper, we present an approach based on Open Learner Model visualization as a first step for making the learning content recommendation process more transparent. A preliminary analysis of students who used the visualization for navigating the content of an introductory programming course showed that considerable time was spent exploring the explanatory interface, which could be linked to the significant likelihood of opening/attempting the recommended activities.