SUNFIT: A Machine Learning-Based Sustainable University Field Training Framework for Higher Education

Author:

Gollapalli Mohammed1ORCID,Rahman Atta2ORCID,Alkharraa Mariam2ORCID,Saraireh Linah3,AlKhulaifi Dania2,Salam Asiya Abdus1,Krishnasamy Gomathi1,Alam Khan Mohammad Aftab4,Farooqui Mehwash4,Mahmud Maqsood5ORCID,Hatab Rehan6

Affiliation:

1. 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

2. 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

3. Department of Management Information System (MIS), College of Business Administration, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia

4. 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

5. Business Analytic Program, Department of Management and Marketing, College of Business Administration, University of Bahrain, Sakhir 32038, Bahrain

6. 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%.

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

Reference35 articles.

1. Students’ Awareness and Embracement of Soft Skills by Learning and Practicing Teamwork;Ragonis;J. Inf. Technol. Educ. Innov. Pract.,2020

2. A Software Development Capstone Course and Project for CIS Majors;Mills;J. Comput. Inf. Syst.,2008

3. ABET Accreditation Program (2022, March 03). Accreditation Board for Engineering and Technology. Available online: https://www.abet.org/.

4. Ingredients of a High-Quality Information Systems Program in a Changing IS;Lending;J. Inf. Syst. Educ.,2019

5. National Center for Academic Accreditation and Evaluation (NCAAA) (2022, January 19). Education and Training Evaluation Commission, Available online: https://etec.gov.sa/en.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3