Assisting Teaching Assistants with Automatic Code Corrections

Yana Malysheva; Caitlin Kelleher

doi:10.1145/3491102.3501820

Assisting Teaching Assistants with Automatic Code Corrections

Yana Malysheva
, Caitlin Kelleher

Department of Computer Science & Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

Undergraduate Teaching Assistants(TAs) in Computer Science courses are often the first and only point of contact when a student gets stuck on a programming problem. But these TAs are often relative beginners themselves, both in programming and in teaching. In this paper, we examine the impact of availability of corrected code on TAs' ability to find, fix, and address bugs in student code. We found that seeing a corrected version of the student code helps TAs debug code 29% faster, and write more accurate and complete student-facing explanations of the bugs (30% more likely to correctly address a given bug). We also observed that TAs do not generally struggle with the conceptual understanding of the underlying material. Rather, their difficulties seem more related to issues with working memory, attention, and overall high cognitive load.

Original language	English
Title of host publication	CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450391573
DOIs	https://doi.org/10.1145/3491102.3501820
State	Published - Apr 29 2022
Event	2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 - New Orleans, United States Duration: Apr 30 2022 → May 5 2022

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings

Conference

Conference	2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
Country/Territory	United States
City	New Orleans
Period	04/30/22 → 05/5/22

Access to Document

10.1145/3491102.3501820

Cite this

@inproceedings{a41b9ca076cd4c28ade8e65bf48887f8,

title = "Assisting Teaching Assistants with Automatic Code Corrections",

abstract = "Undergraduate Teaching Assistants(TAs) in Computer Science courses are often the first and only point of contact when a student gets stuck on a programming problem. But these TAs are often relative beginners themselves, both in programming and in teaching. In this paper, we examine the impact of availability of corrected code on TAs' ability to find, fix, and address bugs in student code. We found that seeing a corrected version of the student code helps TAs debug code 29\% faster, and write more accurate and complete student-facing explanations of the bugs (30\% more likely to correctly address a given bug). We also observed that TAs do not generally struggle with the conceptual understanding of the underlying material. Rather, their difficulties seem more related to issues with working memory, attention, and overall high cognitive load.",

author = "Yana Malysheva and Caitlin Kelleher",

note = "Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 ; Conference date: 30-04-2022 Through 05-05-2022",

year = "2022",

month = apr,

day = "29",

doi = "10.1145/3491102.3501820",

language = "English",

series = "Conference on Human Factors in Computing Systems - Proceedings",

publisher = "Association for Computing Machinery",

booktitle = "CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems",

}

Malysheva, Y & Kelleher, C 2022, Assisting Teaching Assistants with Automatic Code Corrections. in CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems., 231, Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery, 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022, New Orleans, United States, 04/30/22. https://doi.org/10.1145/3491102.3501820

Assisting Teaching Assistants with Automatic Code Corrections. / Malysheva, Yana; Kelleher, Caitlin.
CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2022. 231 (Conference on Human Factors in Computing Systems - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Assisting Teaching Assistants with Automatic Code Corrections

AU - Malysheva, Yana

AU - Kelleher, Caitlin

PY - 2022/4/29

Y1 - 2022/4/29

N2 - Undergraduate Teaching Assistants(TAs) in Computer Science courses are often the first and only point of contact when a student gets stuck on a programming problem. But these TAs are often relative beginners themselves, both in programming and in teaching. In this paper, we examine the impact of availability of corrected code on TAs' ability to find, fix, and address bugs in student code. We found that seeing a corrected version of the student code helps TAs debug code 29% faster, and write more accurate and complete student-facing explanations of the bugs (30% more likely to correctly address a given bug). We also observed that TAs do not generally struggle with the conceptual understanding of the underlying material. Rather, their difficulties seem more related to issues with working memory, attention, and overall high cognitive load.

AB - Undergraduate Teaching Assistants(TAs) in Computer Science courses are often the first and only point of contact when a student gets stuck on a programming problem. But these TAs are often relative beginners themselves, both in programming and in teaching. In this paper, we examine the impact of availability of corrected code on TAs' ability to find, fix, and address bugs in student code. We found that seeing a corrected version of the student code helps TAs debug code 29% faster, and write more accurate and complete student-facing explanations of the bugs (30% more likely to correctly address a given bug). We also observed that TAs do not generally struggle with the conceptual understanding of the underlying material. Rather, their difficulties seem more related to issues with working memory, attention, and overall high cognitive load.

UR - https://www.scopus.com/pages/publications/85130549619

U2 - 10.1145/3491102.3501820

DO - 10.1145/3491102.3501820

M3 - Conference contribution

AN - SCOPUS:85130549619

T3 - Conference on Human Factors in Computing Systems - Proceedings

BT - CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery

T2 - 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022

Y2 - 30 April 2022 through 5 May 2022

ER -

Assisting Teaching Assistants with Automatic Code Corrections

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this