James Skripchuk

College of Engineering

Works (4)

Updated: October 1st, 2024 05:02

2024 article

An Investigation of the Drivers of Novice Programmers' Intentions to Use Web Search and GenAI

20TH ANNUAL ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH, ICER 2024, VOL 1, pp. 487–501.

By: J. Skripchuk n, J. Bacher n & T. Price n 

author keywords: CS Education; Help-seeking; web-search; student perspectives; GenAI
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 8, 2024

2024 article

Overcoming Barriers in Scaling Computing Education Research Programming Tools: A Developer's Perspective

20TH ANNUAL ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH, ICER 2024, VOL 1, pp. 312–325.

By: K. Tran n, J. Bacher n, Y. Shi *, J. Skripchuk n & T. Price n 

author keywords: scaling research tools; computing education tools; tool developer
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 8, 2024

2023 article

Analysis of Novices' Web-Based Help-Seeking Behavior While Programming

PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 1, SIGCSE 2023, pp. 945–951.

By: J. Skripchuk n, N. Bennett n, J. Zheng, E. Li  n & T. Price n 

author keywords: CS education; help-seeking; web-search; novice programming
TL;DR: It is suggested that novices use a variety of web-search strategies -- some quite unexpected -- with varying degrees of success, suggesting that web search can be a challenging skill for novice programmers. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science; OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 4, 2023

2022 article

Identifying Common Errors in Open-Ended Machine Learning Projects

PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE 2022), VOL 1, Vol. 1, pp. 216–222.

By: J. Skripchuk n, Y. Shi n  & T. Price n 

Contributors: J. Skripchuk n, Y. Shi n  & T. Price n 

author keywords: Computer science education; Machine learning education; Data science
TL;DR: This work qualitatively coded over 2,500 cells of code from 19 final team projects in an upper-division machine learning course to identify what ML errors students struggle with, and found that library usage, hyperparameter tuning, and misusing test data were among the most common errors. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: July 23, 2022

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