Undergraduate Computing Tutors' Perceptions of their Roles, Stressors, and Barriers to Effectiveness

Undergraduate teaching assistants (tutors) are commonly employed in computing courses to help students with programming assignments. Prior research in computing education has reported the benefits of tutoring both for students and for the tutors' own learning. In contrast, recent research that examined actual tutoring sessions has reported that these sessions may be less productive than one might hope, with tutors often just giving students the answers to their problems without trying to teach the underlying concepts. To better understand why tutors may be employing these suboptimal practices, we interviewed ten tutors across early computing courses in higher education to identify their perceived role in these sessions, what stressors and factors influence their ability to perform their job effectively, and what kinds of best practices they learned in their tutor training course. Tutors reported their roles around student learning, gauging student understanding, identifying or providing solutions to students, and providing socioemotional support. They reported their stressors around environmental factors (e.g., number of students waiting to be helped, preparation time, peer-tutor frustrations), internal influences, student behavior, student skill levels, and feeling the need to ''read a student's mind.'' Regarding their tutor training course, Tutors reported learning about interaction guidelines and procedures and question-based problem solving. We conclude by discussing how these results may contribute to the less-effective behaviors seen in prior research and potential ways to improve tutoring in computing courses.

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Publication Details

Published
2025-02-12
DOI
https://doi.org/10.1145/3641554.3701784
Citations
2
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
3.39
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Undergraduate Computing Tutors' Perceptions of their Roles, Stressors, and Barriers to Effectiveness

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2 citations
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article

Undergraduate Computing Tutors' Perceptions of their Roles, Stressors, and Barriers to Effectiveness

Leo Porter, Rachel S. Lim, Philip J. Guo, Sophia Krause-Levy, Jeannie Kim, Ismael Villegas Molina, Audria Montalvo, Apollo Larragoitia
article en
2 citations

Abstract

Undergraduate teaching assistants (tutors) are commonly employed in computing courses to help students with programming assignments. Prior research in computing education has reported the benefits of tutoring both for students and for the tutors' own learning. In contrast, recent research that examined actual tutoring sessions has reported that these sessions may be less productive than one might hope, with tutors often just giving students the answers to their problems without trying to teach the underlying concepts. To better understand why tutors may be employing these suboptimal practices, we interviewed ten tutors across early computing courses in higher education to identify their perceived role in these sessions, what stressors and factors influence their ability to perform their job effectively, and what kinds of best practices they learned in their tutor training course. Tutors reported their roles around student learning, gauging student understanding, identifying or providing solutions to students, and providing socioemotional support. They reported their stressors around environmental factors (e.g., number of students waiting to be helped, preparation time, peer-tutor frustrations), internal influences, student behavior, student skill levels, and feeling the need to ''read a student's mind.'' Regarding their tutor training course, Tutors reported learning about interaction guidelines and procedures and question-based problem solving. We conclude by discussing how these results may contribute to the less-effective behaviors seen in prior research and potential ways to improve tutoring in computing courses.

University of San Diego (US), University of California San Diego (US)
Quality Education
Openalex Percentile: Top 8%
Online Learning and Analytics
3.39
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