Automated faculty evaluation and ranking system: utilizing optical character recognition, named entity recognition, and decision tree for a web-based evaluation and ranking system at Pamantasan ng Lungsod ng Maynila

ABSTRACT: High-quality education is essential for achieving a prosperous and equitable society, and a key component of this is a high-caliber faculty. But with the rapid growth of students nowadays, Pamantasan ng Lungsod ng Maynila (PLM) a first and only chartered and autonomous university funded by a city government of Manila, its manual evaluation is causing a hard time in collecting all the results of the said evaluation. Not all universities today are equipped with the necessary technology to meet theie needs, and the decline in transparency during the hiring process leaves faculties and recruites uncertain about the outcome. Three areas of concern have been indentified in the hiring and promotion for PLM faculty members within their respective departments. Initially, the lack of a systemized document management system leads to difficulty in organizing and accessing valuable information. Secondly, the manual faculty selection process is time-consuming and susceptible to human errors, as the results are still manually tallied. Lastly, the absence of an automated decision support system makes it difficult for evaluators to streamline the faculty evaluation process. EduRate is a web-based application that utilizes machine learning to automate the evaluation of faculty applicants based on their academic qualifications, certifications, publications, and creative works. This technology aims to streamline the process, instantly generating necessary reports, improving workflow efficiency, and reducing, administrative work. The researchers analysed the data gathered using mean, utilizing the ISO/IEC 25010 standard. The participants were full-time and part-time faculty members from the College of Engineering (CoE) at PLM, selected using purposive sampling. Subsequently, applying Slovin’s formula with a margin of error of 5% which was 97 respondents. Based on the summary of findings from a 5-point Likert scale, the research found that functional suitability (mean of 1.87), usability (mean of 1.60), reliability (mean of 1.86), security (mean of 1.81) and maintainability (mean of 1.89) were all rated as extremely to very effective. The research findings highlight the potential of EduRate to significantly enhance the quality of faculty evaluation and decision-making in hiring and promotion. Moreover, this can help institutions save on resources, efficiency on evaluators time, and transparent reports.

Filipiniana

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