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Mirna Ahmed Nachouki
- Associate Professor, Head of Information Department
- Ext: 6636
Dr. Mirna Ahmed Nachouki holds a PhD and MSc in Computer Science from Paul Sabatier University, Toulouse III, France, and a BSc in Computer Science from Pau University, France. With over 22 years of teaching and academic experience, she has significantly contributed to the academic community, particularly at Ajman University. Dr. Nachouki spent 16 years leading the Information Systems Department within the former College of Information Technology. She has actively participated in various committees, making positive contributions at both the College and University levels. Dr. Nachouki has published several research papers in the fields of E-learning, Data Mining, and Cloud Computing. She served as the Acting Dean of the former College of Information Technology from July 2017 to January 2019. Currently, she holds the position of Head of the Information Technology Department within the College of Engineering and Information Technology.
Education
- PhD in Computer Science, University of Paul Sabatier, Toulouse III, France, 1995.
- Master in Computer Science, University of Paul Sabatier, Toulouse III, France, 1991.
- Bachelor of Science in Computer Science, University of Pau, France, 1990.
PhD in Computer Science, University of Paul Sabatier, Toulouse III, France, 1995.
Master in Computer Science, University of Paul Sabatier, Toulouse III, France, 1991.
Bachelor of Science in Computer Science, University of Pau, France, 1990.
Experience
- Head of the Information Technology Department, College of Engineering and Information Technology, Jan. 2019 – Present
- Acting Dean, College of Information Technology, Ajman University, July 2017 – January 2019.
- Head of the Information Systems Department, College of Information Technology, Ajman University, UAE, September 2001 – February 2017.
- Assistant Professor, College of Information Technology, Ajman University, UAE, September 1998 – September 2001.
- Teaching Assistant, Department of Informatics, Toulouse III University, France, September 1992 – June 1995.
Head of the Information Technology Department, College of Engineering and Information Technology, Jan. 2019 – Present
Acting Dean, College of Information Technology, Ajman University, July 2017 – January 2019.
Head of the Information Systems Department, College of Information Technology, Ajman University, UAE, September 2001 – February 2017.
Assistant Professor, College of Information Technology, Ajman University, UAE, September 1998 – September 2001.
Teaching Assistant, Department of Informatics, Toulouse III University, France, September 1992 – June 1995.
Teaching Area
- Object Oriented Programming
- Database Management Systems
- Business Intelligence and Data warehousing
- Software Engineering
- Data Structures and Algorithms
- Information Technology in Business
- Organization of Programming Languages
- Computer Graphics
- Algorithms and Problem Solving
- Graduation Projects
- System Analysis and Design
- Fundamentals of Web Systems
- Introductory Programming
Object Oriented Programming
Database Management Systems
Business Intelligence and Data warehousing
Software Engineering
Data Structures and Algorithms
Information Technology in Business
Organization of Programming Languages
Computer Graphics
Algorithms and Problem Solving
Graduation Projects
System Analysis and Design
Fundamentals of Web Systems
Introductory Programming
Research
- Blended learning
- E-learning
- Databases
- Cloud Computing
- Data Mining
- Blockchain
Blended learning
E-learning
Databases
Cloud Computing
Data Mining
Blockchain
Publications
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“Hashing Generation Using Recurrent Neural Networks for Text Documents”, Innovative Computing, Information and Control - Express Letters Part B: Applications ICIC International, Volume 12, Number 3, March 2021, pp. 231-241.
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“Recurrent Neural Networks for Signature Generation”, 13th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics (CISP-BMEI 2020), 25 November 2020.
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“COST OPTIMIZATION OF PROCURING CLOUD COMPUTING RESOURCES USING GENETIC ALGORITHMS”, Journal of Theoretical and Applied Information Technology, Vol.98. No 08, pp. 1801-1213, April 2020.
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"Academic Dishonesty in Computer and Mathematics Assignments: A Comparative Study”, International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume-8, Issue-6S3, September 2019, pp 227-232.
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"Cloud Capacity Planning Based on Simulation and Genetic Algorithms", Intelligent Systems Conference (IntelliSys), Septembre 2019.
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“Factors Influencing Plagiarism and Collusion in Programming Assignments”, 18th International Conference on Information Technology based Higher Education and Training (ITHET) 2019, 26-27 September 2019.
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“Assessing and Evaluating Learning Outcomes of the Information Systems Program”, World Journal of Educational Research, Volume 4, No. 4, December 2017, pages 524 – 536.
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“Blended Learning at Ajman University of Science and Technology: A Case Study”, Multicultural Awareness and Technology in Higher Education: Global Perspectives. Edited by Tomayess Issa, Pedro Isaias & Piet Kommers. March, 2014, Chapter 11, pages 218 – 242.
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“EVALUATING STUDENT SATISFACTION WITH BLENDED LEARNING IN A GENDER-SEGREGATED ENVIRONMENT”, Journal of Information Technology Education: Research- Volume 11, 2012- , pages 185 – 200.
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“Using Interactive Video Conferencing for Blended Teaching at Ajman University of Science and Technology”, IADIS International Conference on E-Learning, Lisbon (Portugal), July 2012.
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“Using Video Conferencing for Blended Teaching at Ajman University of Science and Technology”, EDULEARN11: International Conference on Education and new Learning Technologies, Barcelona (Spain), July 2011.
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“Towards a blended-learning approach in higher education: A case study of introducing an e-learning experience to the conventional learning methods”, EDULEARN10: International Conference on Education and new Learning Technologies, Barcelona (Spain) July 2010.
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“Semantic Reconciliation in Peer Multi-Data Source Management System”, Thirteenth International Database Engineering & Applications Symposium, IDEAS09, Calibria – Italy - 2009.
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Distance Education during the COVID-19 Pandemic: The Impact of Online Gaming Addiction on University Students’ Performance
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The impact of Video Game Addiction on Students’ Performance During COVID-19 Pandemic
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Evaluating Students’ Cyber Ethics Awareness in a Gender-Segregated Environment Under the Impact of COVID-19 Pandemic
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Particle Swarm Model for Predicting Student Performance in Computing Programs
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JournalMirna Nachouki, Mahmoud Abou Naaj,, Predicting Student Performance to Improve Academic Advising Using the Random Forest Algorithm, International Journal of Distance Education Technologies, Vol: 20, pp.1 - 17, Mar 2022, doi: 10.4018/IJDET.296702
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JournalAbou Naaj, Mahmoud; Nachouki, Mirna , Students’ perception of academic dishonesty in programming courses, Journal of Further and Higher Education, pp.1 - 17, Jul 2022, doi: 10.1080/0309877X.2022.2093630
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JournalKouka, S., Makhadmeh, S.N., Al-Betar, M.A., Dalbah, L.M., Nachouki, M., Recent Applications and Advances of Migrating Birds Optimization.", Archives of Computational Methods in Engineering, Aug 2023, doi: 10.1007/s11831-023-09984-z
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JournalAbou Naaj, M., Mehdi, R., Mohamed, E.A., Nachouki, M., Analysis of the Factors Affecting Student Performance Using a Neuro-Fuzzy Approach, Education Sciences, Mar 2023, doi: 10.3390/educsci13030313
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JournalMirna Nachouki, Elfadil A. Mohamed, Riyadh Mehdi, Mahmoud Abou Naaj, Student Course Grade Prediction Using the Random Forest Algorithm: Analysis of Predictors' Importance., Trends in Neuroscience and Education, Vol: 23, Dec 2023
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ConferenceYara Mohammad; Mirna Nachouki, Smart City Requirements to Achieve Safe Smart Transportation Systems in Saudi Arabia., 2023 24th International Arab Conference on Information Technology (ACIT), Ajman, United Arab Emirates, 1
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ConferenceElfadil A. Mohammed; Mahmoud Abou Naaj; Mirna Nachouki; Riyadh Mehdi, Predicting Student Grade Point Average: Comparison of Machine Learning Regression Algorithms., 2023 24th International Arab Conference on Information Technology (ACIT), Ajman, United Arab Emirates, 1
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ConferenceRiyadh Mehdi; Mirna Nachouki, Genetic Algorithm-Based Approach for Predicting Student Academic Success, 2023 24th International Arab Conference on Information Technology (ACIT), Ajman, United Arab Emirates, 1
“Hashing Generation Using Recurrent Neural Networks for Text Documents”, Innovative Computing, Information and Control - Express Letters Part B: Applications ICIC International, Volume 12, Number 3, March 2021, pp. 231-241.
“Recurrent Neural Networks for Signature Generation”, 13th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics (CISP-BMEI 2020), 25 November 2020.
“COST OPTIMIZATION OF PROCURING CLOUD COMPUTING RESOURCES USING GENETIC ALGORITHMS”, Journal of Theoretical and Applied Information Technology, Vol.98. No 08, pp. 1801-1213, April 2020.
"Academic Dishonesty in Computer and Mathematics Assignments: A Comparative Study”, International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume-8, Issue-6S3, September 2019, pp 227-232.
"Cloud Capacity Planning Based on Simulation and Genetic Algorithms", Intelligent Systems Conference (IntelliSys), Septembre 2019.
“Factors Influencing Plagiarism and Collusion in Programming Assignments”, 18th International Conference on Information Technology based Higher Education and Training (ITHET) 2019, 26-27 September 2019.
“Assessing and Evaluating Learning Outcomes of the Information Systems Program”, World Journal of Educational Research, Volume 4, No. 4, December 2017, pages 524 – 536.
“Blended Learning at Ajman University of Science and Technology: A Case Study”, Multicultural Awareness and Technology in Higher Education: Global Perspectives. Edited by Tomayess Issa, Pedro Isaias & Piet Kommers. March, 2014, Chapter 11, pages 218 – 242.
“EVALUATING STUDENT SATISFACTION WITH BLENDED LEARNING IN A GENDER-SEGREGATED ENVIRONMENT”, Journal of Information Technology Education: Research- Volume 11, 2012- , pages 185 – 200.
“Using Interactive Video Conferencing for Blended Teaching at Ajman University of Science and Technology”, IADIS International Conference on E-Learning, Lisbon (Portugal), July 2012.
“Using Video Conferencing for Blended Teaching at Ajman University of Science and Technology”, EDULEARN11: International Conference on Education and new Learning Technologies, Barcelona (Spain), July 2011.
“Towards a blended-learning approach in higher education: A case study of introducing an e-learning experience to the conventional learning methods”, EDULEARN10: International Conference on Education and new Learning Technologies, Barcelona (Spain) July 2010.
“Semantic Reconciliation in Peer Multi-Data Source Management System”, Thirteenth International Database Engineering & Applications Symposium, IDEAS09, Calibria – Italy - 2009.
Distance Education during the COVID-19 Pandemic: The Impact of Online Gaming Addiction on University Students’ Performance
About the journal
The impact of Video Game Addiction on Students’ Performance During COVID-19 Pandemic
About the journal
Evaluating Students’ Cyber Ethics Awareness in a Gender-Segregated Environment Under the Impact of COVID-19 Pandemic
About the journal
Particle Swarm Model for Predicting Student Performance in Computing Programs
About the journal
Mirna Nachouki, Mahmoud Abou Naaj,, Predicting Student Performance to Improve Academic Advising Using the Random Forest Algorithm, International Journal of Distance Education Technologies, Vol: 20, pp.1 - 17, Mar 2022, doi: 10.4018/IJDET.296702
The COVID-19 pandemic constrained higher education institutions to switch to online teaching, which led to major changes in students’ learning behavior, affecting their overall performance. Thus, students’ academic performance needs to be meticulously monitored to help institutions identify students at risk of academic failure, preventing them from dropping out of the program or graduating late. This paper proposes a CGPA predicting model (CPM) that detects poor academic performance by predicting their graduation cumulative grade point average (CGPA). The proposed model uses a two-layer process that provides students with an estimated final CGPA, given their progress in second- and third-year courses. This work allows academic advisors to make suitable remedial arrangements to improve students’ academic performance. Through extensive simulations on a data set related to students registered in an undergraduate information technology program gathered over the years, the authors demonstrate that the CPM attains accurate performance predictions compared to benchmark methods.
About the journal
Abou Naaj, Mahmoud; Nachouki, Mirna , Students’ perception of academic dishonesty in programming courses, Journal of Further and Higher Education, pp.1 - 17, Jul 2022, doi: 10.1080/0309877X.2022.2093630
Plagiarism in programming assignments is a common and current challenge. However, insufficient studies have examined plagiarism in the Middle East region. Thus, this research surveyed 422 students from a middle eastern university. It primarily purported to assess the students’ perception of plagiarism in writing programming assignments. Additionally, this study reported the changes in students’ perceptions of plagiarism in programming assignments between 2018 and 2021, the extent of this dishonest behaviour, and the demographical factors that influence it. A comparative analysis of the data from the 2017–2018 and 2020–2021 surveys of students specialising in Information Technology-related programmes found that those in the latter survey considered plagiarism less acceptable. In addition, the female students and those with a Cumulative Grade Point Average (CGPA) higher than or equal to three also considered cheating and plagiarism behaviours in programming assignments to be less acceptable. Furthermore, these findings did not report a substantial perception variance related to student class standing or specialisation.
About the journal
Kouka, S., Makhadmeh, S.N., Al-Betar, M.A., Dalbah, L.M., Nachouki, M., Recent Applications and Advances of Migrating Birds Optimization.", Archives of Computational Methods in Engineering, Aug 2023, doi: 10.1007/s11831-023-09984-z
In this paper, the recent applications and advances of Migrating Birds Optimization (MBO) algorithm are reviewed. The MBO originated from the V flight shape of the migrating birds in nature to reduce the air pressure and increase the swarm speed. Although MBO is relatively unknown, it has been successfully applied for a plethora of optimization problems in many research fields, such as scheduling, manufacturing, communication and networking, etc. This is due to its impressive characteristics, such as easy-to-use, simple, adaptable and flexible, and sound and complete. Therefore, the growth of MBO is exponentially increased. This review paper considers the changes in the MBO structure, the growth, the foundation and inspiration, the applications, and the limitations. The review ends up with theoretical conclusions about MBO and the possible future directions to cope with the current revolutions in the exponential age.
About the journal
Abou Naaj, M., Mehdi, R., Mohamed, E.A., Nachouki, M., Analysis of the Factors Affecting Student Performance Using a Neuro-Fuzzy Approach, Education Sciences, Mar 2023, doi: 10.3390/educsci13030313
Predicting students’ academic performance and the factors that significantly influence it can improve students’ completion and graduation rates, as well as reduce attrition rates. In this study, we examine the factors influencing student academic achievement. A fuzzy-neural approach is adopted to build a model that predicts and explains variations in course grades among students, based on course category, student course attendance rate, gender, high-school grade, school type, grade point average (GPA), and course delivery mode as input predictors. The neuro-fuzzy system was used because of its ability to implicitly capture the functional form between the dependent variable and input predictors. Our results indicate that the most significant predictors of course grades are student GPA, followed by course category. Using sensitivity analysis, student attendance was determined to be the most significant factor explaining the variations in course grades, followed by GPA, with course delivery mode ranked third. Our findings also indicate that a hybrid course delivery mode has positively impacted course grades as opposed to online or face-to-face course delivery alone.
About the journal
Mirna Nachouki, Elfadil A. Mohamed, Riyadh Mehdi, Mahmoud Abou Naaj, Student Course Grade Prediction Using the Random Forest Algorithm: Analysis of Predictors' Importance., Trends in Neuroscience and Education, Vol: 23, Dec 2023
Background Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to increase student completion and lower dropout rates. Method In this work, we proposed a model based on random forest methodology to predict students' course performance using seven input predictors and find their relative importance in determining the course grade. Seven predictors were derived from transcripts and recorded data from 650 undergraduate computing students. Results Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importance. Course delivery mode does not have a significant effect. Conclusion Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken.
About the journal
Yara Mohammad; Mirna Nachouki, Smart City Requirements to Achieve Safe Smart Transportation Systems in Saudi Arabia., 2023 24th International Arab Conference on Information Technology (ACIT), Ajman, United Arab Emirates, 1
Smart transportation has become a key priority for the Saudi Arabian government in recent years as part of its efforts to modernize its transportation infrastructure and improve mobility for its citizens. Saudi Arabia has implemented several smart transport practices, including intelligent transport systems and technologies, investments in Electric vehicle infrastructure, road safety initiatives, traffic management systems, variable messaging signs, sustainable solutions, and smart parking solutions. The government has also strongly expressed interest in promoting, developing, and deploying Connected and Autonomous Vehicles (CAVs) and Connected, Autonomous, Shared, and Electric (CASE) vehicles. Given the lack of reviews on smart transport initiatives in Saudi Arabia, this paper is a collection of several studies from various databases. This systematic review aims to provide scholars and practitioners access to implemented smart transport and mobility practices within Saudi Arabia. While there is still much work to be done, Saudi Arabia has made significant progress in implementing smart transport practices and is likely to continue investing in this area in the coming years.
About the journal
Elfadil A. Mohammed; Mahmoud Abou Naaj; Mirna Nachouki; Riyadh Mehdi, Predicting Student Grade Point Average: Comparison of Machine Learning Regression Algorithms., 2023 24th International Arab Conference on Information Technology (ACIT), Ajman, United Arab Emirates, 1
Education data mining has emerged as a powerful technique for uncovering hidden patterns in educational data, forecasting academic achievement, and increasing retention rates. In this work, the performance of nine regression algorithms has been evaluated in predicting students’ academic success. Information from 650 students enrolled in three different computing majors has been assembled into a dataset. The following input attributes were chosen: attendance rate, course grade, gender, course category, delivery mode, school type, and high school score; the grade point average was the target variable. Findings indicate that Random Forest Regressor, Light Gradient Boosting Machine, Gradient Boosting Regressor, and Extra Tree are the four most effective regression algorithms in the order given. Except for the Light Gradient Boosting Machine approach, the other three algorithms showed that course grade is the most important predictor of a student's GPA, followed by high school score. All four algorithms showed that gender is the least reliable indicator of GPA. Future work will conduct sensitivity analysis to evaluate the impact of individual attributes on predictions to gain more insight into the factors affecting students’ performance. The effect of social, economic, and demographic features will also be investigated
About the journal
Riyadh Mehdi; Mirna Nachouki, Genetic Algorithm-Based Approach for Predicting Student Academic Success, 2023 24th International Arab Conference on Information Technology (ACIT), Ajman, United Arab Emirates, 1
A student's academic success and on-time graduation, with all the social and economic rewards that come with that, can be significantly improved by predicting a student's performance and the factors contributing to his academic success. There is a limit on how much time an academic adviser can devote to each student to detect curriculum problems, take the appropriate steps, and give the student guidance based on well-informed judgment. Determining whether students are at risk early in the program is essential to improving low-performing students' performance, retention, and completion rates. This will give academic advisers early signals of the need for intervention. In this research, we built and assessed a model based on genetic algorithms to forecast student performance and estimate a student's graduation grade point average (GP A). The model was assessed using a publicly available dataset created for machine learning techniques with R2 = 0.93, indicating it did well. The model was also applied to a dataset made from academic records of students who graduated with a bachelor's degree in computing from our institution; the input predictors were the students' grades in core information technology courses in addition to their high school average and the dependent variable was the graduation grade point average. According to our research, the best predictor of graduation success is student performance in the database management systems course, followed by software engineering, with networking and operating system courses having minimal bearing. Additionally, the findings indicate that only 54% of the graduation grade point average can be explained by the predictors used; hence, other academic and sociodemographic factors will need to be considered in future studies.
About the journal
Conference Presentation
- "Acquisition de connaissances par le tuteur intelligent et pour le tuteur intelligent," 9e Congrès Reconnaissance des Formes et Intelligence Artificielle, RFIA'94, Paris, France, 1994.
- "A Knowledge Acquisition System to Resolve Ambiguous Solutions in Intelligent Tutoring Systems," The Second World Congress on Expert Systems, SWCES'94, Lisbon, Portugal, 1994.
- "Un processus d’acquisition de connaissances dans les tuteurs intelligents," International Conference on Expert Systems Tools & Applications: EXPERSYS-93, IITT-International, Paris, France, 1993.
- "Process Automation Tool for Academic Advising," IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2019), December 10-12, 2019.
"Acquisition de connaissances par le tuteur intelligent et pour le tuteur intelligent," 9e Congrès Reconnaissance des Formes et Intelligence Artificielle, RFIA'94, Paris, France, 1994.
"A Knowledge Acquisition System to Resolve Ambiguous Solutions in Intelligent Tutoring Systems," The Second World Congress on Expert Systems, SWCES'94, Lisbon, Portugal, 1994.
"Un processus d’acquisition de connaissances dans les tuteurs intelligents," International Conference on Expert Systems Tools & Applications: EXPERSYS-93, IITT-International, Paris, France, 1993.
"Process Automation Tool for Academic Advising," IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2019), December 10-12, 2019.
Memberships, Awards and Honors
- ACM membership.
- Moodle Educator Certification