The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula

A Master of Science thesis in Engineering Systems Management by Basel Obaid entitled, “The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula”, submitted in July 2019. Thesis advisor is Dr. Salwa Beheiry. Soft and hard copy available.

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Obaid, Basel (author)
التنسيق: doctoralThesis
منشور في: 2019
الموضوعات:
الوصول للمادة أونلاين:http://hdl.handle.net/11073/16548
الوسوم: إضافة وسم
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author Obaid, Basel
author_facet Obaid, Basel
author_role author
dc.contributor.none.fl_str_mv Beheiry, Salwa
dc.creator.none.fl_str_mv Obaid, Basel
dc.date.none.fl_str_mv 2019-12-15T09:57:16Z
2019-12-15T09:57:16Z
2019-07
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv 35.232-2019.40
http://hdl.handle.net/11073/16548
dc.language.none.fl_str_mv en_US
dc.subject.none.fl_str_mv Artificial Intelligence
Big Data
Engineering Curricula
ABET
Continuous Improvement Process
Accreditation Board for Engineering and Technology (ABET)
dc.title.none.fl_str_mv The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/doctoralThesis
description A Master of Science thesis in Engineering Systems Management by Basel Obaid entitled, “The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula”, submitted in July 2019. Thesis advisor is Dr. Salwa Beheiry. Soft and hard copy available.
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oai_identifier_str oai:repository.aus.edu:11073/16548
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spelling The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering CurriculaObaid, BaselArtificial IntelligenceBig DataEngineering CurriculaABETContinuous Improvement ProcessAccreditation Board for Engineering and Technology (ABET)A Master of Science thesis in Engineering Systems Management by Basel Obaid entitled, “The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula”, submitted in July 2019. Thesis advisor is Dr. Salwa Beheiry. Soft and hard copy available.Higher education institutions generate huge caches of data that can be pivotal in creating value for the next generation. The excellence of these institutions and the engineering programs they provide, their continuous improvement and, above all, the sustainability of engineering education can be ensured if big data, current and dynamic, heterogeneous and large in volume, is collected, analysed and evaluated accurately. Engineering Programs can satisfy the Accreditation Board for Engineering and Technology (ABET) criteria by leveraging the latest disruptive technologies, such as Artificial Intelligence and Big Data Mining, to achieve cost efficiency and to develop better processes for the continuous improvement of the accredited programs. Data analysis helps programs showcase their efforts and help ABET assess the institutions’ conformity to the set standards and provide feedback as well. Above all, the integration of AI in the ABET framework will help in reducing the human involvement and assess the student outcomes in relation to the course learning outcomes. Better decision-making, decision control, trend forecasting, and greater participation of the educational program constituents are some of the other advantages. The primary aim of this research was to develop a framework to integrate AI and Big Data techniques in the continuous improvement process. Subsequently, a metric entitled the Artificial Intelligence Engineering Curricula Index (AIECI) was developed to measure the adoption level of AI and Big Data in the ABET continuous improvement process. This thesis used the existing literature body to amalgamate different AI applications that can be solidly linked to the ABET continuous improvement process. Furthermore, experts from the educational sector were solicited to validate the importance of each AI tool and its link to the ABET continuous improvement process using the Relative Importance Index (RII). Finally, rank sum, reciprocal rank and rank exponent were used to specify weight for each tool based on the results obtained from RII. The results show that learning analytics and gap analysis can be referred to as the most important application for AI with RII of 0.92. Lastly, Performance Prediction was ranked last, with an RII of 0.640.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM)Beheiry, Salwa2019-12-15T09:57:16Z2019-12-15T09:57:16Z2019-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2019.40http://hdl.handle.net/11073/16548en_USoai:repository.aus.edu:11073/165482025-06-26T12:30:44Z
spellingShingle The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula
Obaid, Basel
Artificial Intelligence
Big Data
Engineering Curricula
ABET
Continuous Improvement Process
Accreditation Board for Engineering and Technology (ABET)
status_str publishedVersion
title The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula
title_full The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula
title_fullStr The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula
title_full_unstemmed The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula
title_short The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula
title_sort The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula
topic Artificial Intelligence
Big Data
Engineering Curricula
ABET
Continuous Improvement Process
Accreditation Board for Engineering and Technology (ABET)
url http://hdl.handle.net/11073/16548