Faculty evaluation and qualification analysis system using naive bayes algorithm / Ismil, Lemuel John, Pinauin, Amiel Joaquin, Solano, Patricia Mae. 6
By: Ismil, Lemuel John, Pinauin, Amiel Joaquin, Solano, Patricia Mae. 4 0 16 [, ] | [, ] |
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Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; January 2024.46Edition: Description: 28 cm. ix, 117 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Subject(s): | -- 2 -- 0 -- -- | -- -- 2 -- 0 -- 6 -- | -- 2 0 -- | -- 20 --Genre/Form: Additional physical formats: DDC classification: | LOC classification: | | 2Other classification: In this study, the researchers developed a system that utilizes the Naïve Bayes Algorithm to predict which of the applicants has a resume that qualifies as faculty in a few top private universities in the Philippines namely Centro Escolar University (CEU) and De La Salle-College of Saint Benilde. This will significantly apply analytics in faculty hiring decision-making and help hasten the traditional process used by Human Resource Departments when determining whether or not applicants meet the requirements for open positions inside their institution, the list of qualified applicants will be integrated into the system with filtering and sorting feature to help the Human Resource Department of the educational institution locate applicants in particular categories or fields of specialization where they intend to hire.| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
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Undergraduate Thesis (BS Information Technology) - Pamantsan ng Lungsod ng Maynila, c2024. 56
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ABSTRACT:
Educational Institutions continue to seek excellence in their hiring practices given the vast spectrum of applicants ready to pursue a profession. Considering the degree to which student achievement is impacted by teacher effectiveness, this is significant. This prime perception makes it clear that there is an increasing requirement to investigate and comprehend how we may enhance the hiring procedure at educational institutions.
Choosing the most qualified applicants to join the teaching profession is crucial. The fast-evolving digital world has presented significant challenges for the conventional methods of evaluating faculty applications. The traditional means of assessing applicants qualifications-resumes and interviews, for example-have grown laborious and time-consuming. This raises the possibility of missing out on highly qualified candiates or accepting those who might not be the best fit for the educational duty.
In this study, the researchers developed a system that utilizes the Naïve Bayes Algorithm to predict which of the applicants has a resume that qualifies as faculty in a few top private universities in the Philippines namely Centro Escolar University (CEU) and De La Salle-College of Saint Benilde. This will significantly apply analytics in faculty hiring decision-making and help hasten the traditional process used by Human Resource Departments when determining whether or not applicants meet the requirements for open positions inside their institution, the list of qualified applicants will be integrated into the system with filtering and sorting feature to help the Human Resource Department of the educational institution locate applicants in particular categories or fields of specialization where they intend to hire.
Utilizing the ISO 25010 evaluation, the web-based system shows excellent Functional Suitability, Performance Efficiency, Usability, and Reliability, with respective means of 3.41, 3.26, 3.42, and 2.42.
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