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

doi:10.20469/ijtes.4.10003-5

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…The OCR technology has been very useful in many fields such as spelling corrections [30], generating course advisory in College [31], and AI for Healthcare [32] as it digitized text images needed for researches. A study by Hubert et al created a technique for detecting whether or not a picture includes adverts they focused their analysis on detecting promotional offers made available by firms on social media [33].…”

Section: Optical Character Recognitionmentioning

confidence: 99%

OCR-based Hybrid Image Text Summarizer using Luhn Algorithm with FinetuneTransformer Modelsfor Long Document

Singco¹,

Trillo²,

Abalorio³

et al. 2023

IJETAE

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The accessibility of an enormous number of image text documents on the internet has expanded the opportunities to develop a system for image text recognition with text summarization. Several approaches used in ATS in the literature are based on extractive and abstractive techniques; however, few implementations of the hybrid approach were observed. This paper employed state-of-the-art transformer models with the Luhn algorithm for extracted texts using Tesseract OCR. Nine models were generated and tested using the hybrid text summarization approach. Using ROUGE metrics, we compared the proposed system finetune abstractive models against existing abstractive models that use the same dataset Xsum. As a result, the finetune model got the highest ROUGE score during evaluation; in ROUGE-1 score was 57%, the ROUGE-2 score was 43%, and the ROUGE-L score was 42%. Furthermore, even when better algorithms and models were available for summarization, the Luhn algorithm and T5 finetune model provided significant results.

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Section: Optical Character Recognitionmentioning

confidence: 99%

OCR-based Hybrid Image Text Summarizer using Luhn Algorithm with FinetuneTransformer Modelsfor Long Document

Singco¹,

Trillo²,

Abalorio³

et al. 2023

IJETAE

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“…Abalorio and Cerna [21] proposed the course evaluation generator (CEG) model to address registration and advising problems using (OCR). This allowed the system to read directly from the student's academic transcript.…”

Section: Literature Reviewmentioning

confidence: 99%

Decision-Making Strategy for Digital Transformation: A Two-Year Analytical Study and Follow-Up Concerning Innovative Improvements in University e-Services

Brdesee

Alsaggaf

2022

JTAER

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Universities worldwide strive to provide the best student services possible, particularly those that support student achievements and career goals. Therefore, academic advising continues to be a significant part of the student experience, one which universities need to fully understand in terms of its objectives, application processes, and required skill. As a result of significant technological improvements since the turn of the millennium, including expanding internet applications and digital transformations, universities have established computer information systems that support academic advising and course registration services. This study examined the effects of modifications to the electronic academic advising and course registration systems at King Abdulaziz University in 2018, and then again in 2020, following a university-wide system failure in 2018 resulting from a demand overload. In 2018, a preliminary statistical analysis and student feedback survey were conducted by the authors to measure student satisfaction with the online portal On-Demand University Services (ODUS Plus). In addition to recommendations suggested by the 2018 analysis such as balancing the load distribution of the university’s network, organizational (i.e., non-technical) solutions, rules, and regulations were adjusted such as progressive course registration that prioritized those expected to graduate first. The survey and analysis were repeated by the authors in 2020 to assess improvements in student satisfaction. As a result of the changes, the investigation revealed improved student satisfaction with the performance of ODUS Plus and network access. Overall, students were significantly more satisfied in 2020 than in 2018. This research shows that some technical challenges can be resolved using re-engineered processes and organizational solutions.

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“…It is clear that universities and scientific researchers need to work seriously and give more consideration to fill the gaps in the academic advising systems research and to improve it in a way that allows advising to achieve its purposes and goals. In fact, most of the authors agreed that the electronic academic advising domain is rapidly growing and that it is an active area for researches that will continue improving and contributing with valuable works to develop effective advising systems that serve both students and educational institutions [9], [44]. Fig.…”

Section: Rule-based Expert Systemmentioning

confidence: 99%

From Traditional to Intelligent Academic Advising: A Systematic Literature Review of e-Academic Advising

Assiri¹,

AL-Ghamdi²,

Brdesee³

2020

IJACSA

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Academic advising plays a crucial role in the achievement of the educational institution purposes. It is an essential element in solving students' academic problems and maximizing their satisfaction and loyalty. Universities around the world have always tried to improve academic advising to personalize the student's experience. In fact, technology has the power to improve the advising process and facilitate its corresponding tasks and this has historically taken different forms. Accordingly, this paper provides an overview of academic advising and the technologies proposed to improve it. The authors present a systematic literature review on research papers that proposed an electronic academic advising system to view the research trends and identify electronic academic advising major challenges. The main contribution of this paper is to survey the different aspects and trends about the electronic systems that have been proposed to serve academic advising. This paper is a part of major research that aims to transfer the traditional academic advising to one based on Artificial Intelligence, via the current phase of academic advising.

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

Cited by 5 publications

References 7 publications

OCR-based Hybrid Image Text Summarizer using Luhn Algorithm with FinetuneTransformer Modelsfor Long Document

OCR-based Hybrid Image Text Summarizer using Luhn Algorithm with FinetuneTransformer Modelsfor Long Document

Decision-Making Strategy for Digital Transformation: A Two-Year Analytical Study and Follow-Up Concerning Innovative Improvements in University e-Services

From Traditional to Intelligent Academic Advising: A Systematic Literature Review of e-Academic Advising

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