Author
Listed:
- Mohammed Gollapalli
(Department of Computer Information Systems (CIS), College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)
- Atta Rahman
(Department of Computer Science (CS), College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)
- Mariam Alkharraa
(Department of Computer Science (CS), College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)
- Linah Saraireh
(Department of Management Information System (MIS), College of Business Administration, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)
- Dania AlKhulaifi
(Department of Computer Science (CS), College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)
- Asiya Abdus Salam
(Department of Computer Information Systems (CIS), College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)
- Gomathi Krishnasamy
(Department of Computer Information Systems (CIS), College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)
- Mohammad Aftab Alam Khan
(Department of Computer Engineering (CE), College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)
- Mehwash Farooqui
(Department of Computer Engineering (CE), College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)
- Maqsood Mahmud
(Business Analytic Program, Department of Management and Marketing, College of Business Administration, University of Bahrain, Sakhir 32038, Bahrain)
- Rehan Hatab
(Department of Computer Science, The University of Jordan, Amman 11942, Jordan)
Abstract
With the rapid advances in Information Technology (IT), the focus on engaging computing students to gain practical experience in the IT industry before graduation is becoming increasingly complex without incorporating pedagogical strategies of success in curricula. The goal is to enable computing major students to gain in-depth knowledge and practical understanding of the IT working environment before graduating through essential industry-driven practical skills based on international standards and best practices. Unfortunately, tracking and analyzing students’ practical skills performance during their IT field training programs, which are conducted primarily off-campus at various public and private organizations, before, during, and after the training period, is a daunting task for both the college instructors and the industry trainers. To overcome these challenges, this paper introduces a Sustainable University Field Training (SUNFIT) framework, which is a pedagogical approach towards mining the educational data using machine learning to integrate and measure the field training programs against the internationally recognized accreditation standards such as Accreditation Board for Engineering and Technology (ABET). The study employs machine learning models aimed at continuously measuring and monitoring international ABET accreditation requirements on computing major courses’ academic data, elucidating student performance across various semesters, integrating best practices, and producing an evidence-based rationale approach for evaluating weak learning outcomes (LOs) with minimal manual intervention, as well as preventing faculty-specific portfolio errors. The proposed approach could be easily developed by academics, researchers, or even students, and for a variety of purposes, including enhancing poor student outcomes (SOs). In addition, various data mining and machine learning approaches have been investigated over field training assessment data for successful prediction in subsequent cycles. The results are promising, with Naïve Bayes obtaining the highest accuracy of 90.54% followed by J48 and PART algorithms at 87.83%.
Suggested Citation
Mohammed Gollapalli & Atta Rahman & Mariam Alkharraa & Linah Saraireh & Dania AlKhulaifi & Asiya Abdus Salam & Gomathi Krishnasamy & Mohammad Aftab Alam Khan & Mehwash Farooqui & Maqsood Mahmud & Reha, 2023.
"SUNFIT: A Machine Learning-Based Sustainable University Field Training Framework for Higher Education,"
Sustainability, MDPI, vol. 15(10), pages 1-18, May.
Handle:
RePEc:gam:jsusta:v:15:y:2023:i:10:p:8057-:d:1147674
Download full text from publisher
References listed on IDEAS
- Abdullah Almurayh & Saqib Saeed & Nahier Aldhafferi & Abdullah Alqahtani & Madeeha Saqib, 2022.
"Sustainable Education Quality Improvement Using Academic Accreditation: Findings from a University in Saudi Arabia,"
Sustainability, MDPI, vol. 14(24), pages 1-17, December.
- Abdullah M. Almuhaideb & Saqib Saeed, 2020.
"Fostering Sustainable Quality Assurance Practices in Outcome-Based Education: Lessons Learned from ABET Accreditation Process of Computing Programs,"
Sustainability, MDPI, vol. 12(20), pages 1-26, October.
Full references (including those not matched with items on IDEAS)
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