ORCID
https://orcid.org/0000-0002-6461-7611
Date of Award
Spring 5-15-2023
Degree Name
Master of Science (MS)
Degree Type
Thesis
Abstract
As Teaching Assistant (TA) programs grow in number and size in introductory CS courses, TAs play a significant role in novice programmers' experience and contribute to their success. However, many TAs are also relative beginners themselves and thus have limited experience in programming and teaching. Thus the effectiveness and consistency of their guidance can vary significantly. To improve interaction quality and assist TAs in providing better support, we examine the difficulties encountered by inexperienced TAs in previous literature and then identify the potential for the high cognitive load as an unaddressed difficulty that may prevent new TAs from initiating effective TA-student interactions. This work aims to build a scaffolding tool to assist with assistant teaching tasks and help them initiate better TA-student interactions. A user study has also been conducted for evaluation purposes. Based on our user study, it appears that our assistive interface achieved significant effects on assisting TAs in evaluating students' debugging skills. When performing this task, 80% of TAs achieved better performance after using the assistive interface, and the accuracy of questions answered using the assistive interface is 22% higher than using the baseline interface. Additionally, the assistive interface is more likely to identify visually minor, student-specific problems to facilitate conversations between TAs and students.
Language
English (en)
Chair
Caitlin Kelleher
Committee Members
Caitlin Kelleher, Jonathan Shidal, Doug Shook