arXiv subjects

Sehrish Basir Nizamani

Publications and source records attributed to Sehrish Basir Nizamani.

6 recordsLinked to original sources

arXiv2026

Virtual Global Collaboration in Data Analytics and Machine Learning Education: A Mixed-Methods Study of Asynchronous Cross-Border Teamwork

This innovative practice full paper examines a structured Virtual Global Collaboration (VGC) activity between undergraduate computing courses in the United States and Pakistan. The project was designed to support technical and intercultural skill development through asynchronous international teamwork in data analytics and introductory machine learning. Students worked in mixed-institution teams through a six-phase project including cultural orientation, dataset selection, data cleaning, analysis, introductory machine learning, and structured reporting. Using shared computational tools, teams coordinated across time zones to complete a joint data-driven project. Survey and qualitative reflection data were analyzed to examine collaborative experience, communication and cultural dynamics, learning outcomes, perceived value, and global readiness. Results show consistently positive student experiences across both cohorts, with collaborative processes strongly associated with learning and perceived value, and cross-cultural communication emerging as the primary driver of global readiness. These findings demonstrate how short-term, structured VGC can be effectively integrated into computing courses to support both technical learning and global competence.

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arXiv2026

Understanding Student Use of Large Language Models Across Computer Science Subfields

This research full paper examines how undergraduate students use large language models (LLMs) across computer science subfields. As LLMs become increasingly integrated into computing education, understanding how their use varies across technical and pedagogical contexts is essential for designing effective, subfield-aware instruction. This paper presents a cross-subfield analysis of LLM usage among 211 undergraduate students in a problem-solving course intentionally designed to support responsible and effective LLM use through structured instruction and reflection. Using post-assignment reflection data collected across seven instructional modules spanning multiple computer science subfields, we examine prompt counts, LLM role conceptualization, and verification behavior. Results show that LLM adoption varies substantially by assignment, with higher usage in algorithms and web development and lower usage in software engineering. Students predominantly treat LLMs as assistive tools rather than authoritative sources, and verification is common across all subfields, with most students using multiple strategies. Verification behavior also varies by assignment context, with testing more common in structured tasks and web search more common in open-ended tasks. These findings suggest that assignment characteristics play a central role in shaping how students interact with and evaluate LLM outputs, even under a single, consistently applied instructional design. This work contributes empirical evidence on how LLM adoption, role conceptualization, and verification behavior vary across computer science subfields, extending our prior work on structured, reflective LLM instruction to show how its effects differ by task rather than only in aggregate.

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arXiv2026

WIP: DBWorkout: A Gamified SQL Practice Platform to Support Formative Learning in Database Courses

This research WIP paper presents DBWorkout, a web-based platform that supports formative SQL learning through sandbox-based execution, automated result-based feedback, and session-based gamification. Learning Structured Query Language (SQL) remains challenging for undergraduate students due to limited opportunities for interactive practice and immediate feedback. Students iteratively practice SQL on live database instances while receiving multi-dimensional feedback on query correctness, including row values, column structure, and ordering. To reduce instructor workload, DBWorkout incorporates large language model (LLM)-assisted tools for schema and task generation within a human-in-the-loop workflow. A pilot study with teaching assistants and a classroom deployment involving 170 undergraduate students across two in-class sessions show strong perceived learning value (90% agreement) and engagement (87% enjoyment), alongside low reported pressure (22%). However, only 42% of students found the automated feedback sufficiently actionable, a finding independently corroborated by 40% of open-ended responses raising feedback quality concerns, providing cross-method triangulation of this gap. These findings demonstrate the technical feasibility and early pedagogical potential of DBWorkout while identifying directions for enhancing feedback quality and supporting sustained SQL learning.

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arXiv2026

Less Deliberate in Teams: Student LLM Use Across Individual and Collaborative Work

As large language models (LLMs) become common in computing courses, we need to understand how the social setting shapes how students use them. This paper reports findings from a semester-long study of 96 undergraduate students who completed six assignments, alternating between individual homework and team project milestones. We tracked LLM usage, prompting habits, and how students verified AI-generated output across all six assignments. LLM usage dropped by 42.7 percentage points when students moved from individual work to their first team milestone, then partly recovered in later team tasks. Students also wrote fewer prompts, used fewer deliberate prompting strategies, and checked LLM output less carefully. The share of students who ran tests on AI-generated code fell by 19.4 percentage points during team assignments and never fully rebounded. A within-student analysis found that 18.9% of students who consistently used LLMs on their own stopped using them entirely in teams, while only 3.2% went the other direction. These results suggest that collaborative context is associated with reduced deliberate LLM engagement beyond what task type alone can explain. The moment students form teams appears to be a critical turning point that may benefit from more explicit instructional support.

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arXiv2026

Demystify, Use, Reflect, Assess (DURA): An Experience Report on LLM Integration in CS2

Student access to Large Language Models (LLMs) is reshaping learning behaviors; at the same time students are entering the workforce where effective LLM use is becoming an expected skill. In this Experience Report we share our DURA framework (Demystify-Use-Reflect-Assess) and materials we used to restructure our CS2 course to allow the use of LLMs. We first demystified LLMs, then provided guidance on use with required attribution. We also added reflections related to LLM use at three points throughout the semester to encourage student meta-cognition around LLM use. We increased the value of proctored assessments in tandem with allowing retakes and including questions that explicitly assess skills from programming assignments. Students reported using LLMs for clarifying course concepts, debugging, understanding assignment guidelines, and determining test cases, but also still sought assistance via office hours and TAs, monitored Piazza, and reviewing course content. Students articulated thoughtful and strategic approaches to LLM use and also valued the instructional content and guidance from course staff. Student use of office hours increased slightly this semester and student perceptions that the instructor cares about them and their learning improved.

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arXiv2025

Demystify, Use, Reflect: Preparing students to be informed LLM-users

We transitioned our post-CS1 course that introduces various subfields of computer science so that it integrates Large Language Models (LLMs) in a structured, critical, and practical manner. It aims to help students develop the skills needed to engage meaningfully and responsibly with AI. The course now includes explicit instruction on how LLMs work, exposure to current tools, ethical issues, and activities that encourage student reflection on personal use of LLMs as well as the larger evolving landscape of AI-assisted programming. In class, we demonstrate the use and verification of LLM outputs, guide students in the use of LLMs as an ingredient in a larger problem-solving loop, and require students to disclose and acknowledge the nature and extent of LLM assistance. Throughout the course, we discuss risks and benefits of LLMs across CS subfields. In our first iteration of the course, we collected and analyzed data from students pre and post surveys. Student understanding of how LLMs work became more technical, and their verification and use of LLMs shifted to be more discerning and collaborative. These strategies can be used in other courses to prepare students for the AI-integrated future.

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