Students of today are exposed to technologies that are either educationally effective or distractive. Most of them are having a hard time learning in a traditional classroom setup, are easily distracted, and have difficulty remembering lessons just learned and prerequisite skills needed in learning new lessons. Game-Based Intelligent Tutoring System (GB-ITS) is a technology that provides an individualized learning experience based on student’s learning needs. GB-ITS mimics a teacher doing one-on-one teaching, also known as tutoring, which is more cost-efficient than human tutors. This study developed a general-purpose Memory Enhancer Games system, in a form of a GB-ITS. This study was conducted at Calasiao Comprehensive National High School, identified the game type that best enhances memory and the game features for this proposed system through a questionnaire by (9) ICT teacher respondents. The developed system in this study has undergone validity testing by (8) ICT teachers and professors from Schools Division I of Pangasinan, and of a University in Dagupan City, and acceptability testing by (100) senior high school students of Calasiao Comprehensive National High School, 1st semester of school year 2022-2023, using Likert scale to determine its appropriateness as an intelligent learning tool. The results of the game design questionnaire confirmed the studies of which elements were ideal for a GB-ITS, and both the validity and acceptability survey questionnaires with overall weighted means of 4.57 and 4.08, show that the system is a valid and acceptable intelligent learning tool. The developed MEG can also be of use for testing game features for educational effectiveness and can also contribute to any future study which will conduct to test whether a general-purpose GBL or GB-ITS model would compare; if won’t equal the effectiveness of GBLs designed for delivering specific contents or subjects.

This paper presents an intelligent tutoring system as seen to be successful in assisting in the instruction of basic skill, particularly, reading comprehension. The goal of the study is to develop an Intelligent Tutoring System that will greatly help the Grade 7. The system adapted considerable instructional needs of learners from early development to advanced reading comprehension skills. The developed system provided an immediate feedback to learners upon completion of an activity without requiring intervention from a Teacher. To improve the system, learners and teachers filled out survey questionnaires. The result reveals that teachers and students want the system to be user-friendly, have a user log-in, lesson content with text, audio and video as well as various types of questions in quizzes. They also perceived that the developed ITS is useful and the content is valid thus is very acceptable to be utilized by the learners. In addition, result reveals that student’s reading comprehension could be improved and developed by the proposed ITS.

Cyberbullying is an intentional action of harassment along the complex domain of social media utilizing information technology online. This research experimented unsupervised associative approach on text mining technique to automatically find cyberbullying words, patterns and extract association rules from a collection of tweets based on the domain / frequent words. Furthermore, this research identifies the relationship between cyberbullying keywords with other cyberbullying words, thus generating knowledge discovery of different cyberbullying word patterns from unstructured tweets. The study revealed that the type of dominant frequent cyberbullying words are intelligence, personality, and insulting words that describe the behavior, appearance of the female victims and sex related words that humiliate female victims. The results of the study suggest that we can utilize unsupervised associative approached in text mining to extract important information from unstructured text. Further, applying association rules can be helpful in recognizing the relationship and meaning between keywords with other words, therefore generating knowledge discovery of different datasets from unstructured text.