Course Evaluation Generator (CEG): An Automated Academic Advising System with Optical Character Recognition

Cristopher C. Abalorio and Monalee dela Cerna
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Cristopher C. Abalorio: Program Head for Computer Science, ACLC College of Butuan, Butuan City, Philippines
Monalee dela Cerna: Program Chair in College of Engineering and Information Technology, Surigao State College of Technology, Surigao City, Philippines

International Journal of Technology and Engineering Studies, 2018, vol. 4, issue 5, 189-196

Abstract: Course credit is affected when a student changes in majors, and it is often required additional coursework to meet the academic degree requirement in the tertiary level. Even though the Commission on Higher Education (CHED) has arranged a policy on how to credit courses to college transfers, the Higher Education Institutions (HEI) in the Philippines do not have a unified system in course crediting rules. Course Evaluation Generator (CEG) solved the issues encountered in course advising which are: incorrectness in crediting courses; manual automation in generating courses to offer; and manual evaluation of the remaining courses of a program. The CEG used Tesseract Optical Character Recognition (OCR) to read text from the academic transcript (TOR) by transforming the text into a digital image. To test the prototype constantly, this paper used the RAD approach in the development. The system was tested, deployed, and evaluated by the user respondents. Using the ISO 25010, the weighted mean for accuracy is 3.14 that user-respondents have strongly agreed to the correctness on generating courses to offer for a student. Even though the system itself has met the required functions and features using the Tesseract the Respondents and Researchers suggest that more advanced machine learning should be integrated to enhance this study for future research.

Keywords: Academic advising system; OCR (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:apa:ijtess:2018:p:189-196

DOI: 10.20469/ijtes.4.10003-5

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