Online python tutor: embeddable web-based program visualization for cs education

By Philip J. Guo. 2013

501 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems and Management, Human-Computer Interaction, Software Engineering

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Citations in the corpus

Comparing Code Explanations Created by Students and Large Language Models (2023, background): “Some strategies include trace execution [6], explanations [33], and notional machines [15].”
Students’ Misconceptions and Other Difficulties in Introductory Programming (2017, methodology): “For example, Online Python Tutor is a well-known program visualization tool that is widely used by instructors who teach Python-based college-level CS1 courses (Guo 2013).”
CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs (2024, unknown): “Prerequi-sites of the course include a software design course taught in Java and introductory programming using Python.”
OverCode: visualizing variation in student solutions to programming problems at scale (2014, unknown): “During the previous step, the Python execution logger [Guo 2013] records the values of all in-scope variables after every statement execution in the Python program.”
Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization (2016, background): “Visualizations of program behavior have been shown to improve learnability [19], and Reactive Vega’s dataflow graph offers an execution model that can be readily visualized.”
OverCode (2015, unknown): “Even though all of the solutions that we process in this pipeline have already been marked correct by an autograder, the program tracing [Guo 2013] and renaming scripts occasionally generate errors while processing a solution.”
Generating Diverse Code Explanations using the GPT-3 Large Language Model (2022, methodology): “To provide high-quality explanations at scale, numerous systems automate the process by tracing the execution of code [8, 12], defining terms [9], giving hints [16], and providing error-specific feedback [10, 16].”
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization (2020, methodology): “These tools often graphically represent data structures and algorithms using interactive visualization and animations [7, 14, 18].”