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).
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DeepCode: An Annotated Set of Instructional Code Examples to Foster Deep Code Comprehension and Learning
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.
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- Award ID(s):
- 1822752
- NSF-PAR ID:
- 10367910
- Editor(s):
- Crossley, Scott; Popescu, Elvira
- Date Published:
- Journal Name:
- Proceedings of 18th International Conference on Intelligent Tutoring Systems
- Page Range / eLocation ID:
- 36-50
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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