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We present here a novel instructional resource, called DeepCode, to support deep code comprehension and learning in intro-to-programming courses (CS1 and CS2). DeepCode is a set of instructional code examples which we call a codeset and which was annotated by our team with comments (e.g., explaining the logical steps of the underlying problem being solved) and related instructional questions that can play the role of hints meant to help learners think about and articulate explanations of the code. While DeepCode was designed primarily to serve our larger efforts of developing an intelligent tutoring system (ITS) that fosters the monitoring, assessment, and development of code comprehension skills for students learning to program, the codeset can be used for other purposes such as assessment, problem-solving, and in various other learning activities such as studying worked-out code examples with explanations and code visualizations. We present here the underlying principles, theories, and frameworks behind our design process, the annotation guidelines, and summarize the resulting codeset of 98 annotated Java code examples which include 7,157 lines of code (including comments), 260 logical steps, 260 logical step details, 408 statement level comments, and 590 scaffolding questions. 

Automated program repair is a promising approach to deliver feedback to novice learners at scale. CLARA is an effective repairer that uses a correct program to fix an incorrect program. CLARA suffers from two main issues: rigid matching and lack of support for typical constructs and tasks in introductory programming assignments. We present several modifications to CLARA to overcome these problems. We propose approximate graph matching based on semantic and topological information of the programs compared, and modify CLARA’s abstract syntax tree processor and interpreter to support new constructs and tasks like reading from/writing to console. Our experiments show that, thanks to our modifications, we can apply CLARA to real-world programs. Also, our approximate graph matching allows us to repair many incorrect programs that are not repaired using rigid program matching.