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The ability to automatically assess learners' activities is the key to user modeling and personalization in adaptive educational systems.The work presented in this paper opens an opportunity to expand the scope of automated assessment from traditional programming problems to code comprehension tasks where students are requested to explain the critical steps of a program. The ability to automatically assess these self-explanations offers a unique opportunity to understand the current state of student knowledge, recognize possible misconceptions, and provide feedback. Annotated datasets are needed to train Artificial Intelligence/Machine Learning approaches for the automated assessment of student explanations. To answer this need, we present a novel corpus called SelfCode which consists of 1,770 sentence pairs of student and expert self-explanations of Java code examples, along with semantic similarity judgments provided by experts. We also present a baseline automated assessment model that relies on textual features. The corpus is available at the GitHub repository (https://github.com/jeevanchaps/SelfCode). 

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. 

This paper introduces a new type of smart learning content, an automatically generated trace table, that can easily integrate and adapt to existing curriculum and learning systems for computer science education. In addition to current features of the software, we describe how this tool constructs trace tables using only source code as an input. The potential of this tool is also explored by examining future opportunities in adaptation, feedback, and learning specifications. Last, we report a pilot integration into an existing system to demonstrate interoperability with a tangible use case. 

This paper introduces a new type of smart learning content, an automatically generated trace table, that can easily integrate and adapt to existing curriculum and learning systems for computer science education. In addition to current features of the software, we describe how this tool constructs trace tables using only source code as an input. The potential of this tool is also explored by examining future opportunities in adaptation, feedback, and learning specifications. Last, we report a pilot integration into an existing system to demonstrate interoperability with a tangible use case.