arXiv subjects

Saad Nizamani

Publications and source records attributed to Saad Nizamani.

7 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

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

Can LLMs Reason About Attention? Towards Zero-Shot Analysis of Multimodal Classroom Behavior

Understanding student engagement usually requires time-consuming manual observation or invasive recording that raises privacy concerns. We present a privacy-preserving pipeline that analyzes classroom videos to extract insights about student attention, without storing any identifiable footage. Our system runs on a single GPU, using OpenPose for skeletal extraction and Gaze-LLE for visual attention estimation. Original video frames are deleted immediately after pose extraction, thus only geometric coordinates (stored as JSON) are retained, ensuring compliance with FERPA. The extracted pose and gaze data is processed by QwQ-32B-Reasoning, which performs zero-shot analysis of student behavior across lecture segments. Instructors access results through a web dashboard featuring attention heatmaps and behavioral summaries. Our preliminary findings suggest that LLMs may show promise for multimodal behavior understanding, although they still struggle with spatial reasoning about classroom layouts. We discuss these limitations and outline directions for improving LLM spatial comprehension in educational analytics contexts.

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arXiv2019

Crime Analysis using Open Source Information

In this paper, we present a method of crime analysis from open source information. We employed un-supervised methods of data mining to explore the facts regarding the crimes of an area of interest. The analysis is based on well known clustering and association techniques. The results show that the proposed method of crime analysis is efficient and gives a broad picture of the crimes of an area to analyst without much effort. The analysis is evaluated using manual approach, which reveals that the results produced by the proposed approach are comparable to the manual analysis, while a great amount of time is saved.

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arXiv2019

On the computational models for the analysis of illicit activities

This paper presents a study on the advancement of computational models for the analysis of illicit activities. Computational models are being adapted to address a number of social problems since the development of computers. Computational model are divided into three categories and discussed that how computational models can help in analyzing the illicit activities. The present study sheds a new light on the area of research that will aid to researchers in the field as well as the law and enforcement agencies.

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arXiv2014

A Conceptual Framework for ERP Evaluation in Universities of Pakistan

The higher education has been greatly impacted by worldwide trends. In a result, the universities throughout the world are focusing to enhance performance and efficiency in their workings. Therefore, the higher education has moved their systems to Enterprise Resource Planning (ERP) systems to cope with the needs of changing environment. However, the literature review indicates that there is void on the evaluation of success or failure of ERP systems in higher education Institutes in Pakistan. In overall, ERP systems implementation in higher education of Pakistan has not been given appropriate research focus. Thus, in this paper the authors have attempted to develop a conceptual framework for ERP evaluation in Universities of Pakistan. This seeks to expand the knowledge on ERP in higher educational institutes of Pakistan and focuses on understanding the ERP related critical success factors.

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