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Understanding Support 
Needs in Computer 
Science Labs

Analyzed the integration of AI orchestration tools in computer science labs, uncovering key themes and insights from the experiences of instructors, lab assistants, and students.


Problem Identification:

  • Practical lab sessions in computer science education struggle to effectively bridge the gap between theoretical knowledge and real-world problem-solving.

  • Students often find it challenging to apply theoretical concepts to solve concrete problems, especially when working alone or in paired programming setups. 

  • Instructors and lab assistants face difficulties in efficiently managing time and identifying crucial moments to offer additional support to students.

  • The absence of a single correct approach in computer science complicates timely evaluation and feedback provision.

Additional Challenges in Online Learning:

  •  The physical disconnect in online class settings exacerbates the difficulty of providing timely support.

  • Students face increased struggles with errors, concepts, or debugging without the immediate resources and guidance of a traditional classroom

  • The lack of direct interaction can reduce students' enthusiasm and hinder their learning progress.

The Motivation 

  • There's a pressing need for an orchestration tool in lab settings to bridge the communication and support gap between instructors, lab assistants, and students.

  • Such a system should enable seamless coordination and interaction, enhancing the learning experience in various environments, including online platforms.


3 Months (June - Aug 2023)


Overleaf, Google Doc, Jamboard


In this research, I conducted 17 semi-structured
interviews with students, lab assistants, and instructors to develop a holistic understanding of challenges and potential enhancements in computer science labs, whether they take place face-to-face or remotely, to understand what effective support may look like. 


During the interview, interviewees engaged in the following tasks

  • Share your experiences with CS labs

  • Understand the past challenges

  • Expressing desired future scenarios

  • Roles during a cs lab session

    • In this section, interviewees are engaged in categorizing various tasks and responsibilities from a lab session into a Venn diagram, reflecting the roles of students, instructors, lab assistants, and others (tools), while discussing and explaining their reasoning behind each allocation and sharing personal experiences related to these responsibilities and the tools used to fulfill them.​

Lab Assistant3
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After conducting the interview, I segmented the data into separate notes. I employed affinity diagramming to analyze the data with a bottom-up approach. This involved organizing the labeled notes into a hierarchical structure based on emergent themes. By employing this organized approach, we gained valuable insights and patterns from the data, allowing for a deeper understanding of the users' perspectives in CS labs.



These results highlight that the prevailing approach in CS labs, characterized by one-way, linear communication, appears to hinder the establishment of a cohesive community among stakeholders, particularly students. With this in mind, this work presents potential design considerations for an AI co-orchestration tool that considers four perspectives: instructors, students, AI, and lab assistants. The orchestration system should not just provide support for help giving and receiving but fostering a sense of community to ensure an enriched and practical learning experience.


In undergraduate computer science labs, ineffective collaboration and support pose challenges to comprehensive learning. The purpose of the classes is for students to be able to get real-time support as they learn to apply class concepts. Instructors and lab assistants are often left to monitor the classroom and provide support with limited time or awareness of student activities, which is exacerbated if the class takes place remotely. Recently, orchestration systems have been used to address these issues but have primarily been developed to support mathematics education. Computer science is different in that there are often multiple correct solutions and errors can occur through logical and conceptual misunderstandings, syntax, or more technical issues with the computer. In this research, I conducted 17 semi-structured interviews with students, lab assistants, and instructors to develop a holistic understanding of challenges and potential enhancements in computer science labs including beginning, intermediate, and systems programming courses, whether they take place face-to-face or remotely, to understand what effective support may look like. Using a thematic analysis of these interviews, I found that more awareness is needed around student impasses and more efficient procedures for providing feedback. These results support previous orchestration research findings. Additionally, I found that the lab classes are a way to build a sense of community by enhancing engagement and encouraging active participation, which expands our understanding of how to design computer science lab support beyond previous orchestration systems.

Keywords: Computer Science Labs; Orchestration Systems; Remote and In-person Learning


Xu (Accepted). Understanding Support Needs in Computer Science Labs. ACM Technical Symposium on Computer Science Education.


My research experience with Dr. Jennifer Olsen, delving into the dynamics of computer science labs, has been instrumental in shaping my passion for Human-Computer Interaction (HCI) and computer science (CS). Engaging in 17 semi-structured interviews, I developed a deep understanding of lab challenges and opportunities, specifically in the context of HCI and CS education. My thematic analysis, leading to the paper ‘Understanding Support Needs in Computer Science Labs’ accepted by the ACM Symposium, underscored the necessity for improved student support and feedback mechanisms.


This work, rooted in HCI principles, reinforced my dedication to enhancing educational experiences through technology. It honed my skills in HCI-focused research methods like affinity diagramming, crucial for organizing and interpreting complex data, and solidified my proficiency in qualitative research and academic writing. This journey has not only deepened my understanding of HCI in educational settings but also fueled my desire to pursue further studies in this field, aiming to contribute innovative solutions to HCI challenges.

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