About

Abdussalam Alawini is a Teaching Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His educational background includes a Ph.D. in Computer Science from Portland State University, obtained in 2016, along with two master's degrees—one in Computer Science and another in Engineering and Technology Management—from Portland State University. His professional experience encompasses over six years in industry as a database administrator, lead software developer, and IT manager before returning to academia. His research interests broadly cover databases, applied machine learning, and education. He has focused on building systems to assist scientists in managing file-based datasets by predicting relationships among spreadsheet documents, and has developed data citation and data provenance systems for scientific research. Dr. Alawini is passionate about applying machine learning methods to improve classroom experiences and education in general, as well as building next-generation data management systems, including data provenance, citation, and scientific management systems. His academic career includes positions at the University of Pennsylvania as a postdoctoral researcher, where he contributed to data citation and provenance systems, and teaching roles at Portland State University. His work emphasizes advancing scientific data management and enhancing computer science education.

Research topics

• Computer Science
• Database
• Natural Language Processing
• Programming language
• Data Mining
• Artificial Intelligence
• World Wide Web
• Psychology
• Mathematics education

Selected publications

• Instructor-Aligned Knowledge Graphs for Personalized Learning

  arXiv (Cornell University) · 2026-02-19

  articleOpen accessSenior author

  Mastering educational concepts requires understanding both their prerequisites (e.g., recursion before merge sort) and sub-concepts (e.g., merge sort as part of sorting algorithms). Capturing these dependencies is critical for identifying students' knowledge gaps and enabling targeted intervention for personalized learning. This is especially challenging in large-scale courses, where instructors cannot feasibly diagnose individual misunderstanding or determine which concepts need reinforcement. While knowledge graphs offer a natural representation for capturing these conceptual relationships at scale, existing approaches are either surface-level (focusing on course-level concepts like "Algorithms" or logistical relationships such as course enrollment), or disregard the rich pedagogical signals embedded in instructional materials. We propose InstructKG, a framework for automatically constructing instructor-aligned knowledge graphs that capture a course's intended learning progression. Given a course's lecture materials (slides, notes, etc.), InstructKG extracts significant concepts as nodes and infers learning dependencies as directed edges (e.g., "part-of" or "depends-on" relationships). The framework synergizes the rich temporal and semantic signals unique to educational materials (e.g., "recursion" is taught before "mergesort"; "recursion" is mentioned in the definition of "merge sort") with the generalizability of large language models. Through experiments on real-world, diverse lecture materials across multiple courses and human-based evaluation, we demonstrate that InstructKG captures rich, instructor-aligned learning progressions.

  PublisherOA PDF

• Instructor-Aligned Knowledge Graphs for Personalized Learning

  Open MIND · 2026-02-19

  preprintSenior author

  Mastering educational concepts requires understanding both their prerequisites (e.g., recursion before merge sort) and sub-concepts (e.g., merge sort as part of sorting algorithms). Capturing these dependencies is critical for identifying students' knowledge gaps and enabling targeted intervention for personalized learning. This is especially challenging in large-scale courses, where instructors cannot feasibly diagnose individual misunderstanding or determine which concepts need reinforcement. While knowledge graphs offer a natural representation for capturing these conceptual relationships at scale, existing approaches are either surface-level (focusing on course-level concepts like "Algorithms" or logistical relationships such as course enrollment), or disregard the rich pedagogical signals embedded in instructional materials. We propose InstructKG, a framework for automatically constructing instructor-aligned knowledge graphs that capture a course's intended learning progression. Given a course's lecture materials (slides, notes, etc.), InstructKG extracts significant concepts as nodes and infers learning dependencies as directed edges (e.g., "part-of" or "depends-on" relationships). The framework synergizes the rich temporal and semantic signals unique to educational materials (e.g., "recursion" is taught before "mergesort"; "recursion" is mentioned in the definition of "merge sort") with the generalizability of large language models. Through experiments on real-world, diverse lecture materials across multiple courses and human-based evaluation, we demonstrate that InstructKG captures rich, instructor-aligned learning progressions.

  DOI

• Data Systems Education: Curriculum Recommendations, Course Syllabi, and Industry Needs

  2025-01-22 · 5 citations

  articleOpen access

  Data systems have been an important part of computing curricula for decades, and an integral part of data-focused industry roles such as software developers, data engineers, and data scientists. However, the field of data systems encompasses a large number of topics ranging from data manipulation and database distribution to creating data pipelines and data analytics solutions. Due to the slow nature of curriculum development, it remains unclear (i) which data systems topics are recommended across diverse higher education curriculum guidelines, (ii) which topics are taught in higher education data systems courses, and (iii) which data systems topics are actually valued in data-focused industry roles. In this study, we analyzed computing curriculum guidelines, course contents, and industry needs regarding data systems to uncover discrepancies between them. Our results show, for example, that topics such as data visualization, data warehousing, and semi-structured data models are valued in industry, yet seldom taught in courses. This work allows professionals to further align curriculum guidelines, higher education, and data systems industry to better prepare students for their working life by focusing on relevant skills in data systems education.

  PublisherOA PDFDOI

• Instructional Benefits of a Web-Based Students’ Concurrent Course Registration Tool

  2025-08-21

  article
  PublisherDOI

• Fourth International Workshop on Data Systems Education (DataEd'25)

  2025-06-17

  articleSenior author

  Interest in data systems education is increasing, especially with the rise in demand for well-trained and re-trained data scientists. The database and computing education research communities have complementary perspectives and experiences to share with each other. The DataEd workshop is organized as a dedicated venue for these communities to come together to share findings, cross-pollinate perspectives and methods, and shed light on opportunities for mutual progress in data systems education. In the DataEd 2025 workshop, we will present and discuss data management systems education experiences and research with a keynote speaker, an industry speaker, and paper presentations followed by rich discussions.

  PublisherDOI

• How Students Use Generative AI: Insights from Conversation Log Analysis

  2025-11-02

  articleSenior author

  This paper aims to provide insights into how students interact with Generative Artificial Intelligence (GenAI) and identify patterns in their usage of Illinois Chat, a GenAI educational tool developed by the University of Illinois Urbana Champaign. With advancements in technology and machine learning, GenAI has become increasingly prevalent across various domains, including education. It is evident that students frequently use GenAI tools like ChatGPT in their academic lives; however, there are limited studies analyzing how they integrate these tools into their daily learning practices. To address this gap, we propose a study that examines how students interact with a GenAI tool across different engineering disciplines. Specifically, we aim to identify the common categories of questions students seek help with, discern patterns in their input prompts, and examine potential instances of academic dishonesty involving the GenAI tool. Using both inductive and deductive thematic analysis, we analyzed conversation log data to uncover emerging themes and validate prior findings. Our analysis revealed distinct patterns in how students use the tool to understand concepts, their behavioral trends during interactions, and signs that may indicate academic dishonesty. This study provides insights into how students engage with GenAI and highlights areas for improvement to enhance the educational effectiveness of these tools. Additionally, we outline potential indicators of academic dishonesty, which could help mitigate learning loss in the AI-driven educational landscape.

  PublisherDOI

• CodeLens: A Generative AI Framework for Automated Feedback on SQL Assignments

  2025-06-22 · 1 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Data-Driven Insights into AI-Powered Learning: Analyzing Student Interactions with AI-bot in Engineering Education

  2025-08-21 · 1 citations

  articleSenior author
  PublisherDOI

• Exploring Computing Students' Sense of Belonging Before and After a Collaborative Learning Course

  2024-03-07 · 5 citations

  articleOpen access

  Prior work has found that women tend to report lower sense of belonging compared to men in STEM and computing contexts, which may discourage women's persistence. Collaborative learning has been shown to improve students' sense of belonging in some STEM and computing courses relative to traditional lecturing; however, these studies tend to focus on a single course or the first implementation of such pedagogical changes. Our study explores whether these trends generalize by measuring students' sense of belonging across three non-introductory computing courses that have consistently used collaborative learning activities over three semesters. We ask the following research question: Is collaborative learning generally associated with an increased sense of belonging, especially for women? We found that while there were variations across courses, students' reported sense of belonging improved in all courses. Notably, women's reported sense of belonging improved 15% whereas men's reported sense of belonging improved by 11%. Our findings complement prior studies by providing evidence of a relationship between increased sense of belonging and collaborative learning, and suggest students' sense of belonging is malleable beyond the first year. These findings challenge critiques of past studies as being isolated to single courses or conducted only immediately after an effort to change a course, suggesting pedagogical changes may hold promise in improving students' affective outcomes.

  PublisherOA PDFDOI

• Clustering Entity Relationship Diagrams: Enhancing Feedback Quality and Grading Consistency in Large Database Courses

  2024-10-13

  article

  This innovative practice full paper introduces a tool for clustering Entity Relationship Diagrams (ERDs) and explores its application in large classes. ERDs are fundamental for database design in courses related to databases, data science, and software engineering. However, processing ERD homework submissions in large classes poses significant challenges due to the variety of design decisions made by students, leading to numerous diagram variations. This paper presents an ERD clustering tool designed to group similar ERD submissions, aiding instructors and teaching assistants in identifying popular solutions and common mistakes. The tool employs advanced object detection, OCR, and clustering technologies. We evaluated the tool using four datasets from two large public U.S. universities, with submissions ranging from 130 to 430 diagrams. Various clustering methodologies were assessed, highlighting the importance of incorporating ERD structure into the clustering process. Our findings indicate that the tool successfully generated adequate clusters, and that aiming for 10 clusters is appropriate regardless of the dataset size. The generated clusters included common approaches and mistakes, proving helpful for providing feedback and simplifying the grading process.

  PublisherDOI

Frequent coauthors

• Sophia Yang

  16 shared

• Susan B. Davidson

  University of Pennsylvania

  13 shared

• Geoffrey Herman

  Lahore University of Management Sciences

  10 shared

• Leshang Chen

  8 shared

• Junhyong Kim

  California University of Pennsylvania

  6 shared

• Stephen Fisher

  6 shared

• Gianmaria Silvello

  University of Padua

  5 shared

• Yinjun Wu

  Philadelphia University

  5 shared

Labs

• Data Systems Education: Curriculum Recommendations, Course Syllabi, and Industry NeedsPI

Education

• PhD, Computer Science

  Portland State University

  2016

• MSc., Engineering and Technology Management

  Portland State University

  2011

• MSc., Computer Science

  Portland State University

  2011

Awards & honors

• Celebration of Excellence 2023
• Celebration of Excellence 2024
• Celebration of Excellence 2025
• Celebration of Excellence 2026