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The students' performance in licensure examinations is considered one of the reliable gauges of an institution's efficiency and its students' intellectual capacity. This study explored the factors that affected the performance in the 2019 Licensure Examination for Fisheries Technologists (LEFT) of the Western Philippines University (WPU) Bachelor of Science in Fisheries (BSF) graduates, using Stufflebeam's (2014) CIPP evaluation model. The study analyzed the data from the 24 BSF graduates who joined the institutional review class for LEFT 2019. Findings revealed that the graduates' LEFT performance was significantly influenced by their scores in the pre- and post-mock board examinations, employment, the length of time between graduation and examination, number of attempts in taking the board examination, and the number of the subject areas passed in the licensure exam. The review class offered by the College of Fisheries and Aquatic Sciences (CFAS) of WPU significantly improved the scores of students in the LEFT 2019. Further, several other variables that might be affecting the performance of students in the LEFT 2019 were also identified in this study. Recommendations and implications for improving the WPU-BSF and the CFAS review programs are presented in conclusion.

Accordingly, licensure examinations such as the LEFT have become an important consideration in curriculum planning and instructional implementation since students' performance in board examinations has been equated to the institutions' efficiency as well as their students' intellectual capacity (Manalo & Obligar, 2013). The Accrediting Agency of the Chartered Colleges and Universities in the Philippines (AACCUP) has even included licensure examination performance as an indicator in its quality assurance evaluation instrument. This, perhaps, explains the efforts of HEIs to implement policies and standards that would ensure not only the graduates' competence in their respective fields of discipline but also the success in the board examinations (Antiojo, 2017; Antonio, Malvar, & Ferrer, 2016; Nool & Ladia, 2017).

However, studies show contradicting findings on the impact of institutional review programs on the board performance of graduates. For instance, Riney, Thomas, Williams, and Kelly (2006) found that the in-house review program in their study had decreased the level of motivation of student teachers in improving their academic performance and ratings in board examinations. This claim runs in contrast to the findings of other studies (Duckor Castellano, Tellez, Wihardini, & Wilson, 2014; Pecheone & Chung, 2006; Youngs Odden & Porter, 2003), proving that the implemented policies of in-house reviews of universities and review centers were among the factors that largely influenced the board examinations performance of their respondents.

The surveyed literature shows a significant number of studies exploring the different factors affecting the performance of graduates in licensure examinations. Most of these studies focused on the Licensure Examinations for Teachers (LET). Although there was no particular study directed to the LEFT performance of BSF graduates, the studies presented in this section are deemed relevant since the nature and challenges in taking a licensure examination may also be true to all board exam takers.

Currently, research lenses have shifted focus on the predictors of LET performance. Quiambao Buenviaje, Nuqui, and Cruz's (2015) study, for instance, investigated the factors affecting the licensure examination performance of education graduates at Don Honorio Ventura Technological State University in Bacolor, Pampanga, using survey methods and historical data. The authors claimed that teachers' educational attainment, length of service, library and laboratory facilities, intelligence quotient, and general weighted average are the factors that influenced the performance in the LET. Hence, they recommended that these factors be considered in crafting policies to improve the graduates' board examination performance.

Input evaluation is used to evaluate the steps and resources needed to meet program goals and objectives. In this study, Input evaluation focused on the reviewee variables that might have significantly affected the 2019 LEFT performance, such as academic honor, employment, length of time between graduation and examination, number of attempts, and number of the subject areas passed in the board exam. In the Process evaluation phase, the pre-mock board examination and the mock board examination results were used to determine the effectiveness of the CFAS review program. These results were also analyzed to establish if they were reliable indicators of predicting the reviewees' chance at passing the 2019 LEFT. The Product evaluation phase examined the WPU-BSF graduates' performance in the 2019 LEFT and its possible determinants (i.e., pre-mock board examination and mock board examination results, academic honor, employment, length of time between graduation and examination, number of attempts, and number of the subject area passed in the board exam). Other variables were also elicited through a short interview before and after the 2019 LEFT. The obtained data were provided to the decision-makers for planning, (re) designing, and implementation of the WPU-BSF and CFAS review programs.

A total of 33 graduates of WPU-CFAS took the 2019 board examinations for fisheries technologists. Of the number, 30 were first-time takers, and three (3) were repeaters. Twenty-four among the 33 examinees took the review class offered by the college prior to taking the 2019 LEFT. Thus, the data analyzed in this study were from the 24 students who joined the review class for the 2019 LEFT. The LEFT enhancement review program of CFAS consisted of lectures and activities. It ran for 8 hours every Saturday and Sunday for two months. Each lecture delivered by the college faculty members had a pre-post examination to measure students' retention of information provided by the review class. A survey questionnaire was also used to establish the demographics and the variables that affected their performance in the 2019 licensure examination. Informed consent was secured from the respondents prior to the distribution of the survey questionnaires.

Respondents who took the review class were given a pre-mock board examination prior to the LEFT review to assess their knowledge in the four areas of fisheries (aquaculture, capture fisheries, aquatic resources, and post-harvest fisheries). Each subject area was composed of 100 questions. The post-mock-board exam was also given to the students after the two-month review class. The mean scores of each student in the pre- and post-mock-board were analyzed to determine if students' knowledge of fisheries subjects had increased significantly or not.

A short interview with the respondents was performed before and after taking the board examination. This was carried out to gather the necessary information about their insights on the factors affecting their performance in the 2019 LEFT. Although this study only focuses on the 2019 LEFT performance of the graduates, the data on the number of takers and the passing rate of the WPU-BSF graduates from 2015 to 2019 was also consolidated from the PRC's website for reference.

The multinomial logistic regression at p < 0.05 was used to predict which variables (pre-mock, mock board, length of time between graduation and examination, employment, academic honor, and a number of attempts) determined the performance of BSF graduates in the 2019 LEFT. Other statistical tools such as the omnibus test for model coefficients (Chi-square test, degrees of freedom, and significance) and R squares (Chi-square (x2)) were used to ascertain whether the collected data is distributed in such a way that it matches the established probability distribution. Hosmer-Lemeshow test was also performed to verify if the data fits the model.

Table 2 reflects that the variables tested in this study significantly affected the WPU graduates' performance in the 2019 licensure examination for fisheries technologists ([chi square] = 33.104, df = 7, p < 0.001). This result was strengthened by the results of the multiple logistic regression analysis on the six variables, showing that all of them except academic honor significantly affected the performance results ([chi square] = 0.000, df = 1, p < 1.000).

Based on the data, the scores on the pre and post-mock board exams, employment, length of time between graduation and examination, number of attempts in taking the board exam, and the number of subject areas passed in the board exam significantly affected the performance of the respondents in the licensure examination. Further, Table 4 indicates that the model used in this study fits the data well.

Table 5 indicates that most of the respondents were employed during the time they took the board exam. Based on the result of statistics, employment significantly affected the performance of the students in the 2019 LEFT (p < .001). In an informal interview, it was revealed that unemployed students had more time to read and concentrate on their review materials and activities than the employed ones (Table 5). The respondents also shared that they reread certain books and review materials given to them twice or even thrice because they had more time reviewing. On average, unemployed BSF graduates are allotted around 5-6 hours during weekdays and 8-10 hours on weekends for their review (8 hours under the review class of CFAS and two more hours at home, particularly before sleeping). Rereading lectures or books repeatedly and immediately after a short period increased information retention and the ability of students to answer questions in an examination (Verkoeijen, Rikers, & Ozsoy, 2008). Hence in this present study, unemployed BS Fisheries graduates who spent more time reviewing significantly increased their knowledge in the field, which consequently determined their scores in the 2019 LEFT. One of the 24 respondents graduated with Latin honors; however, it did not significantly affect the performance of the graduates in the 2019 LEFT ([chi square] = 0.000, df = 1, p < 1.000) (Table 3). 2b1af7f3a8

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