Data Analytics on Virtual Learning Environment for Philippine Re-Entry Education Program

This paper aims to present the design of a crowd-sourced virtual learning environment (VLE) using data analytics for the Philippine re-entry education program-- the Alternative Learning System (ALS) of the Department of Education. This was a major response to the United Nations' Sustainable Development Goals target which is to ensure inclusive and quality education for and promote lifelong learning [18]. The system will primarily benefit alternative learners--individuals that have not finished formal education. Under DepEd's ALS [14], these learners get corresponding learning certifications by undergoing an alternative curriculum and passing the Accreditation and Equivalency (A&E) Test. The study made use of the data from nineteen (19) learners' test results of the previous A&E test to determine the distribution of exam questions to be applied in the proposed VLE. Aside from serving as a virtual assistant to the alternative learners, the VLE will also increase efficiency of mobile teachers, and administrators of ALS programs thus improving the delivery of the Philippine re-entry education program. Learners' test results showed that all students, regardless of whether they passed the exam or not, had a hard time in the Communication Skills and the Critical Thinking and Problem Skills Learning Strands. These two most difficult strands are also the ones with the highest weights in score distribution. The proposed design allows for greater training and practice for learners in the difficult learning strands in the VLE. The study was able to show the design feasibility of an intelligent tutoring system that is adaptable to both individual learners and external factors, and is cost efficient due to the crowd sourced content. Further studies may explore system's adaptability to unique behavioral characteristics of users.