About

Dr. Mohammed Farghally is a Collegiate Assistant Professor in the Department of Computer Science at Virginia Tech. Born in Cairo, Egypt, he completed his primary, preparatory, and secondary education in Assiut, Egypt, before earning a bachelor's degree in Information Systems from the College of Computers and Information at Assiut University. He began his academic career as a teaching assistant and assistant lecturer at Minia University, where he was responsible for lab and recitation sessions for undergraduate students. Concurrently, he pursued a master's degree at Cairo University, which he received in 2009. Subsequently, he was awarded a governmental scholarship to pursue PhD studies in the United States through the VT-MENA program, an agreement between the Egyptian Ministry of Higher Education and Virginia Tech. At Virginia Tech, he earned a second master's degree and a PhD, during which he worked with Professor Cliff Shaffer on the OpenDSA project, focusing on developing and evaluating innovative methods for presenting algorithm analysis topics in CS3-level courses to enhance student engagement.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Machine Learning
• Mathematics
• Data Mining
• Medical education
• Engineering ethics
• Physics
• Engineering management
• Algorithm
• Structural engineering
• Mathematics education
• Psychology
• Medicine

Selected publications

• Detecting Credit-Seeking Behavior with Programmed Instruction Framesets in a Formal Languages Course

  Education Sciences · 2025-03-31 · 1 citations

  articleOpen accessCorresponding

  When students use an online eTextbook with content and interactive graded exercises, they often display aspects of two types of behavior: credit-seeking and knowledge-seeking. A student might behave to some degree in either or both ways with given content. In this work, we attempt to detect the degree to which either behavior takes place and investigate relationships with student performance. Our testbed is an eTextbook for teaching Formal Languages, an advanced Computer Science course. This eTextbook uses Programmed Instruction framesets (slideshows with frequent questions interspersed to keep students engaged) to deliver a significant portion of the material. We analyze session interactions to detect credit-seeking incidents in two ways. We start with an unsupervised machine learning model that clusters behavior in work sessions based on sequences of user interactions. Then, we perform a fine-grained analysis where we consider the type of each question presented within the frameset (these can be multi-choice, single-choice, or T/F questions). Our study involves 219 students, 224 framesets, and 15,521 work sessions across three semesters. We find that credit-seeking behavior is correlated with lower learning outcomes for students. We also find that the type of question is a key factor in whether students use credit-seeking behavior. The implications of our research suggest that educational software should be designed to minimize opportunities for credit-seeking behavior and promote genuine engagement with the material.

  PublisherOA PDFDOI

• Identifying and addressing common undergraduate database design misconceptions

  Frontiers in Education · 2025-06-24

  articleOpen access

  Data are a crucial asset for organizations, making it essential for database designers to effectively organize and manage data using DataBase Management Systems (DBMS). DataBase design Concepts (DBCs) are central to computer and information sciences curricula, focusing on teaching students how to conceptually and logically design database systems to manage data through the DBMS. Key concepts such as conceptual data modeling and mapping models to relational schemas are fundamental to effective database design. However, students often struggle to grasp these concepts despite existing efforts to improve students' learning. Research is limited to identifying and addressing common misconceptions of students related to the DBCs. In this study, our aim is to fill in this research gap by identifying students' difficulties and challenges in comprehending DBCs, exploring their root causes, and proposing a pedagogical intervention to address these challenges early in the learning process. The primary goal is to gain a deeper understanding of how students learn and apply DBCs, formulate a clear list of students' DBC misconceptions, and finally create an online interactive visual tool aimed at addressing database design learning difficulties and misconceptions through visual presentations.

  PublisherOA PDFDOI

• Categorizing and Comparing Students' Interactions in eTextbooks

  2025-11-02

  article

  The rise of interactive eTextbooks opens new opportunities for enhancing student engagement and learning outcomes. However, analyzing student interactions within these digital platforms remains challenging. This study examines student engagement profiles in OpenDSA, an interactive eTextbook for data structures and algorithms courses. Using session-level interaction data, we categorize engagement into four distinct engagement states, defined as types of student activities: Reading, Visualization, Proficiency Exercises, and Multiple Choice Exercises. We then apply clustering techniques to identify distinct engagement profiles, characterized by the frequency of transitions and total engagement time spent in each engagement state. Our research addresses two key questions: (1) What engagement profiles can be identified from students' interactions across these four engagement states? (2) How do these engagement profiles correlate with students' academic performance? Our findings reveal four distinct engagement profiles: Highly Engaged Learners, exhibiting frequent transitions and high engagement across all engagement states; Moderately Engaged Learners, characterized by sporadic interactions and below-average overall engagement; Balanced Learners, maintaining consistent and moderate engagement across engagement states, and Minimally Engaged Learners, demonstrating limited engagement and infrequent state transitions. Statistical analysis confirms that students in profiles with frequent and diverse engagement significantly outperform minimally engaged learners academically. These results underline the critical role of active, varied engagement in student success and underline the potential of session-level data for monitoring and optimizing student engagement. We believe our findings will be valuable to eTextbook developers, providing actionable insights to guide the design of digital content and targeted interventions that improve student engagement and performance.

  PublisherDOI

• WIP: Examining Geospatial Differences in the Gender Wage Gap in STEM

  2025-11-02

  articleSenior author

  This WIP research study highlights the gender wage gap in STEM careers, a significant issue that has garnered extensive media attention and academic scrutiny in the recent years. While this gap has reduced over the last several decades, it is still far from an equitable distribution. With the availability of little to no prior academic work focusing on the heterogeneous geospatial effects around this pay differential, we explore this phenomenon by utilizing data from a crowd-sourced database where we estimate wages for males and females, controlling for education and experience, across several U.S. states. We find significant geospatial differences in the wage gap and explore how consumers, businesses, and policymakers can utilize these results. Our findings can help STEM educators better understand regional disparities in STEM career outcomes, informing how they advise students and design inclusive programs. In addition, these insights may also support curriculum development that incorporates discussions about equity in the workforce.

  PublisherDOI

• The AI Policy Module: Developing Computer Science Student Competency in AI Ethics and Policy

  2025-11-02

  article
  PublisherDOI

• Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study

  2025-01-22 · 8 citations

  articleOpen access

  Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional development. The majority of the instructors reported positive experiences working with external partners and did not report any issues with Non-Disclosure Agreements (NDAs) or disputes about Intellectual Property (IP). In most institutions, students own the IP of their work, and clients understand that. We use the global perspective that this work has given us to provide guidelines for institutions to better support capstone instructors.

  PublisherDOI

• The Impostor Phenomenon in the Global Computing Graduate Student Population

  2025-09-29 · 1 citations

  articleOpen access

  Several studies have confirmed that undergraduates in computing programs frequently experience the Impostor Phenomenon (IP). However, this work has largely focused on North America and Europe, and no work has evaluated graduate students in computing. This study evaluates the rate of IP experiences in graduate programs globally to determine whether rates of IP experiences are consistent and whether there are institutions or locations with lower rates of IP that might inform the development of support systems to reduce its prevalence. We perform a multi-institutional, multi-national survey-based study of 11 institutions, with at least one on every populated continent. The survey asks graduate students to complete the Clance IP scale (CIPS), which is the standard evaluation instrument for IP, as well as to answer a number of demographic questions that establish their experience level, gender, and ethnicity.We evaluate the overall level of IP experiences at each institution as well as across regions, and we explore the interaction between CIPS scores, region, and demographic factors.

  PublisherOA PDFDOI

• The AI Policy Module: Developing Computer Science Student Competency in AI Ethics and Policy

  2025-11-02 · 1 citations

  articleOpen access

  As artificial intelligence (AI) further embeds itself into many settings across personal and professional contexts, increasing attention must be paid not only to AI ethics, but also to the governance and regulation of AI technologies through AI policy. However, the prevailing post-secondary computing curriculum is currently ill-equipped to prepare future AI practitioners to confront increasing demands to implement abstract ethical principles and normative policy preferences into the design and development of AI systems. We believe that familiarity with the ‘AI policy landscape’ and the ability to translate ethical principles to practices will in the future constitute an important responsibility for even the most technically-focused AI engineers. Toward preparing current computer science (CS) students for these new expectations, we developed an AI Policy Module to introduce discussions of AI policy into the CS curriculum. Building on a successful pilot in fall 2024, in this innovative practice full paper we present an updated and expanded version of the module, including a technical assignment on “AI regulation”. We present the findings from our pilot of the AI Policy Module 2.0, evaluating student attitudes towards AI ethics and policy through pre- and post-module surveys. Following the module, students reported increased concern about the ethical impacts of AI technologies while also expressing greater confidence in their abilities to engage in discussions about AI regulation. Finally, we highlight the AI Regulation Assignment as an effective and engaging tool for exploring the limits of AI alignment and emphasizing the role of ‘policy’ in addressing ethical challenges.

  PublisherOA PDFDOI

• Embedded Ethics in CS: Experiences with Integrating Ethics Assignments in Sophomore, Junior, and Senior Level Courses

  2025-06-13

  articleOpen access1st authorCorresponding

  Technical and ethical aspects of Computer Science (CS) are interdependent. Many CS departments teach ethical and social implications of technology in separate standalone courses. However, prior research shows that ethical issues are better taught in tandem with their related technical content as an integral required skill in CS curricula. In this experience report, we share our experience with embedding ethics assignments in 3 CS courses at different levels: a CS2 course in software design and data structures, a CS3 course in data structures and algorithms, and a Software Engineering capstone course, all taught at Virginia Tech (a large public R1 institution) in Spring 2024. Students from the 3 courses were surveyed at the beginning and end of Spring 2024. By comparing results from the pre and post surveys, we found that the embedded assignments for the CS2 and CS3 courses improved students' confidence in their knowledge about how ethical issues may come into play in their career, their confidence in their ability to address ethical issues arising from applying technology in real contexts, and their confidence in communicating and defending their positions on how to address these issues. For all 3 courses, students gave positive feedback on how the assignments were engaging and relevant to the course, and how it improved their ability in raising, and reasoning about, ethical implications of technology. We believe that the practices and results of our experience will be helpful to other CS instructors thinking of injecting ethical content into their technical courses.

  PublisherDOI

• Experiences of Instructors Who Teach Capstone Courses in Computing Fields

  2024-07-04 · 2 citations

  article

  Capstone courses are an integral part of undergraduate and postgraduate degrees in the computing fields. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self reflection as they transition into the real world. Prior research on capstone courses has primarily focused on the experiences of the students. The perspectives of instructors who teach these capstone courses has not been explored much. However, an instructor's motivation and expectancy can have a significant effect on a capstone course quality. In this working group, we plan to use a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation will be examined through a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across multiple continents. This global perspective will be used to develop a guiding framework on the different pedagogical approaches that can be used to enhance engagement and motivation for both staff and students in computing courses.

  PublisherDOI

Frequent coauthors

• Taysir Hassan A. Soliman

  Assiut University

  19 shared

• Clifford A. Shaffer

  Virginia Tech

  18 shared

• Ahmed I. Taloba

  Al Jouf University

  15 shared

• Ahmed A. Abdelrahman

  Otto-von-Guericke University Magdeburg

  12 shared

• Derek Haqq

  Virginia Tech

  10 shared

• Brett D. Jones

  Centre for Addiction and Mental Health

  10 shared

• Marghany Hassan Mohamed

  Assiut University

  10 shared

• Margaret Ellis

  Virginia Tech

  10 shared

Education

• Ph.D., Computer Science

  University of California, Los Angeles

  2013

• M.S., Computer Science

  University of California, Los Angeles

  2009

• B.S., Computer Science

  University of California, Los Angeles

  2007