DeepCode: An Annotated Set of Instructional Code Examples to Foster Deep Code Comprehension and Learning

Rus, Vasile; Brusilovsky, Peter; Tamang, Lasang Jimba; Akhüseyinoğlu, Kamil; Fleming, Scott

doi:10.1007/978-3-031-09680-8_4

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…The success of self-explanation for text comprehension and learning can be linked to its constructive aspect, e.g., it activates several cognitive processes such as generating inferences to fill in missing information and integrating new information with prior knowledge, monitoring and repairing faulty knowledge, and its meaningfulness for the learner, i.e., self-explanations are self-directed and self-generated making the learning and target knowledge more personally meaningful, in contrast to explaining the target content to others. Self-explaining has demonstrated a positive impact on student learning in a variety of fields, including physics (Conati and VanLehn 2000), math (Aleven and Koedinger 2002), and programming (Bielaczyc, Pirolli, and Brown 1995;Rus et al 2021).…”

Section: Introductionmentioning

confidence: 99%

SelfCode: An Annotated Corpus and a Model for Automated Assessment of Self-Explanation During Source Code Comprehension

Chapagain

Risha

Banjade

et al. 2023

FLAIRS

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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).

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