The study tried to analyze the relationship of the numerical value of the faculty performance rating and the actual observations, opinions, feelings, and description of the students towards the performance of the observed faculty members using text analytics. The result reveals that students describe faculty members with a rating of 1 with negative words. Faculty members with rating 2 were described by the students using neutral words/word patterns. In the case of faculty members with rating 3, positive word/word pattern “good” was used by the students to describe the performance of the faculty members. The results revealed that if a faculty members was evaluated and rated 4 and 5 the descriptions are positive observations / comments from the student respondents. The results reveal not only the quantitative values of faculty evaluation it also exposed the qualitative description of the students in the performance of their faculty members. This study brings out significant aspects of the teaching performance of the faculty members of Pangasinan State University. The results can be used for coaching and mentoring by university and campus heads to their faculty members in terms of their weaknesses. Moreover, the results can be utilized by Pangasinan State University to evaluate the teaching performance of their faculty members based on the comments or opinions of the students.

This paper investigates students’ success at Pangasinan State University by identifying patterns and models that might be used to correctly classify and predict if a student will transfer or finish their studies. In this study, three categorical variables or attributes and one continuous variable were considered independent variables due to the availability of the data. The results from the binary logistic regression model with the high school general average and course as independent variables (Model 3), and the decision tree model with transition gain as a splitting criterion were fitted to the dataset to generate a model that possibly best describes the students’ mobility in Pangasinan State University Urdaneta City Campus. The decision tree model is better than the binary logistic regression model based on accuracy, AUC, and sensitivity values. This implies that the decision tree model is better at correctly classifying observations as "transferred" than Model 3. Thus, it was concluded that the decision tree model with information gain as the splitting criterion best describes the mobility of PSU students. The results of this paper can be used for school administration involving students’ mobility/success, particularly in classifying whether a student will transfer based on other.

Evaluating faculty members' performance is a very complex area to study. In addition, predicting the performance of these faculty members is a very difficult and challenging task. However, the core of education is teaching and learning, and teaching-learning works to its fullest when there are effective teachers. Measuring the effectiveness of faculty members is done based on the student evaluation of faculty. This research aims to develop a model to predict the performance of the faculty members using associative rule based on the existing evaluation form used by PSU to evaluate faculty members. The model is designed to utilize the knowledge of text analytics rule capabilities that will provide great support for the decision-making of Pangasinan State University in the Philippines. The result reveals that the term good is still the top one terms occurred for all campuses followed by teaching. The results indicated that teacher/faculty members on all campuses are good teachers. Associating words reveal that "teaching good subject/topic," "explains simply" and other meaningful associated words can be utilized to evaluate the performance of the teacher. The results exposed not only the quantitative values of faculty evaluation it also exposed the qualitative opinion of the students in the performance of their faculty members. This study reveals important aspects of the faculty member's teaching performance in terms of words/association of words that will describe their teaching performance. The results can be utilized in coaching and mentoring faculty members to cope with their weaknesses. The proposed model can be utilized by Pangasinan State University to evaluate the faculty members in terms of their teaching performance by utilizing the comments/opinions of the students.

Cyberbullying has become one of the major threats in our society today due to the massive damage that it can cause not only in the cyber world and the internet-based business but also in the lives of many people. The sole purpose of cyberbullying is to hurt and humiliate someone by posting and sending threats online. However, recognition of cyberbullying has proved to be a hard and challenging task for information technologists. The main objective of this study is to analyze and decode the ambiguity of human language used in cyberbullying Lesbian, Gay, Bisexual, Transgender and Queer or Questioning (LGBTQ) victims and detect patterns and trends from the results to produce meaning and knowledge. This study will utilize an unsupervised associative approach text analysis technique that will be used to extract the relevant information from the unstructured text of cyberbullying messages. Furthermore, cyberbullying incidence patterns will be analyzed based on recognizing relationships and meaning between cyberbullying keywords with other words to generate knowledge discovery. “Fuck” and “Shit” account almost half of all cyberbullying words and appear more that 75 % in the dataset as the most frequently used words. Further, the terms “shit”+“hate”+ “fuck” with a positive lift value and “shit”+ “stupid” positive obtained the highest chance of togetherness / chance of utilizing both of these words to cyber bully. The combination of words / word patterns was considered abusive swearing is always considered rude when it is used to intimidate or humiliate someone. The output and results of this study will contribute to formulating future intervention to combat cyberbullying. Furthermore, the results can be utilized as a model in the development of a cyberbullying detection application based on the text relations / associations of words in the comments, replies, blog discussion and discussion groups across the social networks.