Academic Analytics is extracting hidden patterns from educational databases. The main goal of this area is to extract hidden patterns from student academic performances and behaviors. One of the main topics in academic analytics is to study the academic performance of freshman students. Students enrolled in first year are the most vulnerable to low student retention in higher education institution. Research studies from different Higher Educational Institutions already indicated that early identification of students with academic difficulty is very crucial in the development of intervention programs. As such, early identification of potential leavers and successful intervention program(s) are the keys for improving student retention. The study will utilize the available enrollment and admission data. Feature selection technique will be used to determine significant attributes. The study aims to produce predictive and cluster model in which can early identify students who are in need of academic help and program interventions. The extracted predictive and cluster models will be evaluated using confusion matrix and be integrated in the decision support application.

The study aims to create an application using openCV that can identify and calculate the vegetation part of a satellite image. The study utilized the two out of seven Landsat-8 band images over a part of Metro Manila in Philippines acquired on Feb. 13, 2016. These band images are Bands 4 and 5 which are normally used to compute the vegetation index of a satellite image. The algorithm calculates the relative area of the vegetation using Normalized Difference Vegetation Index or NDVI formula. The output of the NDVI creates a single-band dataset that only shows greenery. Values close to zero represent rock and bare soil and negative values represent water, snow and clouds. Taking ratio or difference of two bands makes the vegetation growth signal differentiated from the background signal. Water has an NDVI value less than 0, bare soils between 0 and 0.1, and vegetation over 0.1. Increase in the positive NDVI value means greener the vegetation. The sample satellite image is Manila with Landsat-8 operational land imager (OLI) images. The study aims to test the NDVI formula in extracting vegetation index of Manila region which can be used to monitor the urban and classification of a certain region.

Event Management Solution is a web application for EINS Consultancy that allows users to manage events, announcements, and content of the website. Management includes creation, deletion, update, and view. Both the System Manager and Administrator have the privilege to manage events, announcements, and content of the website. Only the System Manager has the privilege to manage user accounts and view the audit trail. While the end-user is able to view the created events and posted announcements. Also, an end-user could register to available events through an electronic form. EINS event management solution is a platform that handles event management and registration modules. With the above mentioned features, the system can produce easy access reports from the centralized repository of data. This leads to lessen or illuminate inaccurate reports from the current processes used by EINS Consultancy. The researcher used prototyping model as a software developmental technique in developing the application. Different test levels were conducted in order to determine the acceptability of the software which include alpha, beta and user acceptance test.

The E- Learning System is a supplementary educational tool to help students to improve their academic achievement. E-learning is the integration of technology in education that covers a variety of activities to support the teaching and learning practices in educational settings. One of the main purposes of e-learning system is to allow teachers to define and manage contents and learning resources on the web. The current e-learning infrastructure of FEU Institute of Technology (Moodle) follows a one size fits all learning framework. This framework is providing similar learning preferences, which lead to similar learning preferences. As a result, students with learning difficulty of the subject have higher percentage to fail in a given course. Addressing this problem is crucial, as learning management system's main goal is to improve student learning capability from different types of learners. With this, the researcher developed e-learning platform in which mainly driven with individualized and personalized learning tailored to specific requirements and learning preferences. The system extracts students learning paths. These learning paths are very essential in determining difficult topics and courses so proper remediation and intervention can be given to students to improve their academic performance.

The study focused on developing Industry Academe Linkage Alumni and Placement Portal for FEU Institute of Technology that will monitor student progress in their internship and monitor alumni in their job placement. The purpose of the development is to automate the workflow and processes of interns for industry placement and tracking of alumni. The study is designed to eliminate the manual process of alumni tracking, student application for internship and industry job opening postings. The main gist of the project consists of intern monitoring module that monitors who are deployed and accepted already; alumni monitoring module to trace and monitor alumni if there are in their field of specialization and report generation module that it capable of generating dynamic reports of alumni, industry partners and interns. The study used Incremental Model Process as software process model in which is combined by the elements of the waterfall model with the iterative philosophy of prototyping. The proponent considered iterative life cycle model which consists of four phases. To determine the acceptability of the developed prototype ISO 9126 was used. The metrics consist of criteria such as functionality, usability, reliability, portability, and sup portability as perceived by end users. The overall evaluation of the system is 4.21 with an interpretation of Satisfactory. This concludes that the application is ready for deployment.