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Achieving excellence in the fields of education, scientific research, and community service, and promote the university to the level the prestigious universities locally, regionally and globally.
Contributing to building and developing the knowledge community by creating university environment and community partnership that stimulate creativity as well as freedom of thought and expression. Also, keeping abreast of technological developments in the field of education, thus providing the society with the qualified human resources that can meet the needs of the labor market.
The University is committed to consolidating the following fundamental values: 1. Social and moral commitment. 2. Sense of belonging. 3. Justice and equality. 4. Creativity. 5. Quality and Excellence. 6. Transparency and accountability. 7. Responsible freedom. 8.Futurity.
Dr. Ahmed Abu Elaish Doctorate / Associate Professor
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Dr. Ahmad Abu- Al- Aish is assistant profrssor in computer sciece
Mobile and electronic learning systems for higher education/educational technology-
Mobile learning acceptance and deployment in higher education -
Metaheuristics and Combinatorial Optimization-
Assistant Professor Faculty of Computer sciences and Information Technology, Jerash University, Jordan
Taught courses: Introduction to information technology, C++ Programming, Computer skills, Fundamentals of E-commerce, Human computer interaction, OperationResearch, Computation theory, Analysis and design of algorithms, discrete mathematics and software project management.
2010-2012 General Teacher Saudi Student Club, UK
Taught Maths, Chemistry and English language for students in different grades.
2002 - 2007 Mathematics Teacher Ministry of Education, Jordan.
Taught mathematics utilizing advanced teaching methods at different grades, including primary and secondary grades.
Alnabhan, M., Abu-Al-Aish, A. (2014). M-learning QoS Measurement Model: A Context Adaption Approach. In Proceeding of the ICCESEN, October 25-29. Antalya, Turkey
Recent Research Publications 1- An Elite Pool-Based Big Bang-Big Crunch Metaheuristic for Data Clustering Ibrahim Al-Marashdeh, Ghaith M. Jaradat, Masri Ayob, Ahmad Abu-Al-Aish, Mutasem Alsmadi Journal of Computer Science × An Elite Pool-Based Big Bang-Big Crunch Metaheuristic for Data Clustering This paper delves into the capacity of enhanced Big Bang-Big Crunch (EBB-BC) metaheuristic to handle data clustering problems.BB-BC is a product of an evolution theory of the universe in physicsand astronomy. Two main phases of BB-BC are big bang and bigcrunch. The big bang phase involves a creation of a population ofrandom initial solutions, while in the big crunch phase these solutionsare shrunk into one elite solution exhibited by a mass center. This studylooks into enhancing the BB-BC’s effectiveness in clustering data.Where, the inclusion of an elite pool alongside implicit solutionrecombination and local search method, contribute to suchenhancement. Such strategies resulted in a balanced search of goodquality population that is also diverse. The proposed elite pool-basedBB-BC was compared with the original BB-BC and other identicalmetaheuristics. Fourteen different clustering datasets were used to testBB-BC and the elite pool-based BB-BC showed better performancecompared to the original BB-BC. BB-BC was impacted more by theincorporated strategies. The experiments outcomes demonstrate the highquality solutions generated by elite pool-based BB-BC. Its performancein fact supersedes that of identical metaheuristics such as swarmintelligence and evolutionary algorithms. Ibrahim Al-Marashdeh, Ghaith M. Jaradat, Masri Ayob, Ahmad Abu-Al-Aish, Mutasem Alsmadi Journal: 2- Collaborative and ubiquitous mobile learning system prototype Mohammad Alnabhan, Ahmad Abu-Al-Aish, Sultan A. Al-Masaeed International Journal of Computer Applications in Technology Download × Collaborative and ubiquitous mobile learning system prototype Mobile learning applications utilise the advantages of mobile technologies to increase learning opportunities, mainly on an anytime, anywhere basis. The advancement of mobile technology has facilitated the development of numerous applications to improve students’ learning experience and performance. Successful implementation of m-learning is highlydependent on learning context and environment awareness. This work presents a multiphaseexploration of early phases responsible for defining and validating the m-learning context, andlater phases based on context validation results achieved from the previous phases, involving thedevelopment and evaluation of a new m-learning context prototype. This new prototype provedto provide context-aware and ubiquitous learning services fulfilling several diverse userinteraction levels and requirements. Mohammad Alnabhan, Ahmad Abu-Al-Aish, Sultan A. Al-Masaeed Journal: 3- Automating SWOT Analysis Using Machine Learning Methods Ahmad Abu-Al-Aish, Ghaith Jaradat, Mahmoud Al-Shugran, Iyas Alodat International Journal of Advances in Soft Computing and its Applications Download × Automating SWOT Analysis Using Machine Learning Methods Quality assurance is one major concern for the faculty of Computer Science and Information Technology (FCSIT) at Jerash University. It involves eight standards including the strategic planning. SWOT analysis is a method meant for assisting the formulation of strategy and planning. An application to strategic planning process formulation for the FCSIT is described. This research studies the SWOT analysis with a major concern of drawing more conclusions using machine learning methods. Data mining is a subfield of machine learning, which focuses on exploratory data analysis using supervised or unsupervised learning. Data mining techniques help fetching required knowledge from raw data to make decisions more confidently interpreted and automated. In this study, regression, classification, clustering, association rules, attributes selection techniques are used to mine data from the SWOT analysis. Using Weka workbench, results of each technique is obtained and interpreted with the favor of the factors that have impact on the success of the strategic plan. The outcome presents a high level of satisfaction regarding employee, and a vibrant level of satisfaction regarding students. Therefore, the developed quality assurance framework is stable but needs more improvements to overcome the dissatisfaction of many students regarding services, supervision, awards and activities. Ahmad Abu-Al-Aish, Ghaith Jaradat, Mahmoud Al-Shugran, Iyas Alodat Journal: 4- Exploring Technical Quality Factors That Enhance Mobile Learning Applications Services Using Data Mining Techniques Ahmad Abu-Al-Aish International Journal of Information and Communication Technology Education Download × Exploring Technical Quality Factors That Enhance Mobile Learning Applications Services Using Data Mining Techniques Mobile learning (m-learning) has become an increasingly attractive solution for schools anduniversities that utilize new technologies in their teaching and learning settings. This study investigatesthe technical factors affecting the development of m-learning applications services from students’perspectives. It presents a model consisting of 12 technical factors, including content usefulness,scalability, security, functionality, accessibility, interface design, interactivity, reliability, availability,trust, responsiveness, and personalization. To evaluate the model, a questionnaire was designed anddistributed to 151 students in Jerash University, Jordan. The results indicate that all technical factorshave positive effects on learner satisfaction and overall m-learning applications service; however,the data mining analysis revealed that security and scalability factors exert a major impact on studentsatisfaction with m-learning applications services. This study gives insight into the future of developingand designing m-learning applications Ahmad Abu-Al-Aish Journal: 5- Using E-learning System in Jordanian Universities during the COVID-19 Pandemic: Benefits and Challenges Ahmad Abu-Al-Aish Computer and Information Science; Download × Using E-learning System in Jordanian Universities during the COVID-19 Pandemic: Benefits and Challenges During the Coronavirus Disease 2019 (COVID19) pandemic and the national lockdowns implemented incountries around the world, many universities worldwide made the transition from face to face delivery to onlinelearning using e learning systems. However, t he successful transition from traditional class based learning toonline learning depends greatly on understanding the challenges related to the implementation and use ofe learning systems, as well as the technical and management factors that need to be e nhanced. This study aimedto investigate the challenges related to the use of e learning systems in Jordanian universities and to explore thetechnical and management aspects that impacted the successful implementation and use of e learning systemsduring COVID 19. To achieve the study objectives, a questionnaire was developed by the researcher anddistributed online to lecturers working at Jordanian universities. A total of 184 lecturers participated in the study.Based on the findings, the study provides recommendations which will help higher education policy makers,university management teams, and software developers build strategies to ensure the successful implementationand use of e learning systems during the COVID 19 pandemic. Ahmad Abu-Al-Aish Journal:
This paper delves into the capacity of enhanced Big Bang-Big Crunch (EBB-BC) metaheuristic to handle data clustering problems.BB-BC is a product of an evolution theory of the universe in physicsand astronomy. Two main phases of BB-BC are big bang and bigcrunch. The big bang phase involves a creation of a population ofrandom initial solutions, while in the big crunch phase these solutionsare shrunk into one elite solution exhibited by a mass center. This studylooks into enhancing the BB-BC’s effectiveness in clustering data.Where, the inclusion of an elite pool alongside implicit solutionrecombination and local search method, contribute to suchenhancement. Such strategies resulted in a balanced search of goodquality population that is also diverse. The proposed elite pool-basedBB-BC was compared with the original BB-BC and other identicalmetaheuristics. Fourteen different clustering datasets were used to testBB-BC and the elite pool-based BB-BC showed better performancecompared to the original BB-BC. BB-BC was impacted more by theincorporated strategies. The experiments outcomes demonstrate the highquality solutions generated by elite pool-based BB-BC. Its performancein fact supersedes that of identical metaheuristics such as swarmintelligence and evolutionary algorithms.
Mobile learning applications utilise the advantages of mobile technologies to increase learning opportunities, mainly on an anytime, anywhere basis. The advancement of mobile technology has facilitated the development of numerous applications to improve students’ learning experience and performance. Successful implementation of m-learning is highlydependent on learning context and environment awareness. This work presents a multiphaseexploration of early phases responsible for defining and validating the m-learning context, andlater phases based on context validation results achieved from the previous phases, involving thedevelopment and evaluation of a new m-learning context prototype. This new prototype provedto provide context-aware and ubiquitous learning services fulfilling several diverse userinteraction levels and requirements.
Quality assurance is one major concern for the faculty of Computer Science and Information Technology (FCSIT) at Jerash University. It involves eight standards including the strategic planning. SWOT analysis is a method meant for assisting the formulation of strategy and planning. An application to strategic planning process formulation for the FCSIT is described. This research studies the SWOT analysis with a major concern of drawing more conclusions using machine learning methods. Data mining is a subfield of machine learning, which focuses on exploratory data analysis using supervised or unsupervised learning. Data mining techniques help fetching required knowledge from raw data to make decisions more confidently interpreted and automated. In this study, regression, classification, clustering, association rules, attributes selection techniques are used to mine data from the SWOT analysis. Using Weka workbench, results of each technique is obtained and interpreted with the favor of the factors that have impact on the success of the strategic plan. The outcome presents a high level of satisfaction regarding employee, and a vibrant level of satisfaction regarding students. Therefore, the developed quality assurance framework is stable but needs more improvements to overcome the dissatisfaction of many students regarding services, supervision, awards and activities.
Mobile learning (m-learning) has become an increasingly attractive solution for schools anduniversities that utilize new technologies in their teaching and learning settings. This study investigatesthe technical factors affecting the development of m-learning applications services from students’perspectives. It presents a model consisting of 12 technical factors, including content usefulness,scalability, security, functionality, accessibility, interface design, interactivity, reliability, availability,trust, responsiveness, and personalization. To evaluate the model, a questionnaire was designed anddistributed to 151 students in Jerash University, Jordan. The results indicate that all technical factorshave positive effects on learner satisfaction and overall m-learning applications service; however,the data mining analysis revealed that security and scalability factors exert a major impact on studentsatisfaction with m-learning applications services. This study gives insight into the future of developingand designing m-learning applications
During the Coronavirus Disease 2019 (COVID19) pandemic and the national lockdowns implemented incountries around the world, many universities worldwide made the transition from face to face delivery to onlinelearning using e learning systems. However, t he successful transition from traditional class based learning toonline learning depends greatly on understanding the challenges related to the implementation and use ofe learning systems, as well as the technical and management factors that need to be e nhanced. This study aimedto investigate the challenges related to the use of e learning systems in Jordanian universities and to explore thetechnical and management aspects that impacted the successful implementation and use of e learning systemsduring COVID 19. To achieve the study objectives, a questionnaire was developed by the researcher anddistributed online to lecturers working at Jordanian universities. A total of 184 lecturers participated in the study.Based on the findings, the study provides recommendations which will help higher education policy makers,university management teams, and software developers build strategies to ensure the successful implementationand use of e learning systems during the COVID 19 pandemic.
Email : ahmad.abualaish@gmail.com