A Machine Learning Based DSS in Predicting Undergraduate Freshmen Enrolment in a Philippine University

• URL: http://arxiv.org/abs/2108.07690v1
• Date: Thu, 29 Jul 2021 03:54:28 GMT
• Title: A Machine Learning Based DSS in Predicting Undergraduate Freshmen Enrolment in a Philippine University
• Authors: Dr. Joseph A. Esquivel and Dr. James A. Esquivel
• Abstract summary: The sudden change in the landscape of Philippine education, including the implementation of K to 12 program, Higher Education institutions, have been struggling in attracting freshmen applicants. A review of the various characteristics of freshman applicants influencing their admission status at a Philippine university were included in this study. Using Logistic Regression, a predictive model was developed to determine the likelihood that an enrolled student would seek enrolment in the institution.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The sudden change in the landscape of Philippine education, including the implementation of K to 12 program, Higher Education institutions, have been struggling in attracting freshmen applicants coupled with difficulties in projecting incoming enrollees. Private HEIs Enrolment target directly impacts success factors of Higher Education Institutions. A review of the various characteristics of freshman applicants influencing their admission status at a Philippine university were included in this study. The dataset used was obtained from the Admissions Office of the University via an online form which was circulated to all prospective applicants. Using Logistic Regression, a predictive model was developed to determine the likelihood that an enrolled student would seek enrolment in the institution or not based on both students and institution's characteristics. The LR Model was used as the algorithm in the development of the Decision Support System. Weka was utilized on selection of features and building the LR model. The DSS was coded and designed using R Studio and R Shiny which includes data visualization and individual prediction.

Related papers

• Improving On-Time Undergraduate Graduation Rate For Undergraduate Students Using Predictive Analytics [0.0]
  The on-time graduation rate among universities in Puerto Rico is significantly lower than in the mainland United States. This project aims to develop a predictive model that accurately detects students early in their academic pursuit at risk of not graduating on time.
  arXiv  Detail & Related papers  (2024-05-02T22:33:42Z)
• Continual Learning with Pre-Trained Models: A Survey [61.97613090666247]
  Continual Learning aims to overcome the catastrophic forgetting of former knowledge when learning new ones. This paper presents a comprehensive survey of the latest advancements in PTM-based CL.
  arXiv  Detail & Related papers  (2024-01-29T18:27:52Z)
• Intelligent System for Assessing University Student Personality Development and Career Readiness [0.0]
  This research paper explores the impact of various factors on university students' readiness for change and transition. The collected data from a KBTU student survey was processed through machine learning models. An intelligent system was built using these models and fuzzy sets.
  arXiv  Detail & Related papers  (2023-08-29T20:32:58Z)
• Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions [21.70450099249114]
  College admissions offices have historically relied on standardized test scores to organize large applicant pools into viable subsets for review. We explore a machine learning-based approach to replace the role of standardized tests in subset generation. We find that a prediction model trained on past admission data outperforms an SAT-based model and matches the demographic composition of the last admitted class.
  arXiv  Detail & Related papers  (2023-02-07T17:14:26Z)
• Process-BERT: A Framework for Representation Learning on Educational Process Data [68.8204255655161]
  We propose a framework for learning representations of educational process data. Our framework consists of a pre-training step that uses BERT-type objectives to learn representations from sequential process data. We apply our framework to the 2019 nation's report card data mining competition dataset.
  arXiv  Detail & Related papers  (2022-04-28T16:07:28Z)
• Identifying Critical LMS Features for Predicting At-risk Students [4.718094586237028]
  Learning management systems (LMSs) have become essential in higher education. We present an additional use of LMS by using its data logs to perform data-analytics and identify academically at-risk students.
  arXiv  Detail & Related papers  (2022-04-27T22:43:45Z)
• Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin-picking [98.5984733963713]
  We propose an iterative self-training framework for sim-to-real 6D object pose estimation to facilitate cost-effective robotic grasping. We establish a photo-realistic simulator to synthesize abundant virtual data, and use this to train an initial pose estimation network. This network then takes the role of a teacher model, which generates pose predictions for unlabeled real data.
  arXiv  Detail & Related papers  (2022-04-14T15:54:01Z)
• Cognitive Diagnosis with Explicit Student Vector Estimation and Unsupervised Question Matrix Learning [53.79108239032941]
  We propose an explicit student vector estimation (ESVE) method to estimate the student vectors of DINA. We also propose an unsupervised method called bidirectional calibration algorithm (HBCA) to label the Q-matrix automatically. The experimental results on two real-world datasets show that ESVE-DINA outperforms the DINA model on accuracy and that the Q-matrix labeled automatically by HBCA can achieve performance comparable to that obtained with the manually labeled Q-matrix.
  arXiv  Detail & Related papers  (2022-03-01T03:53:19Z)
• Wizard of Search Engine: Access to Information Through Conversations with Search Engines [58.53420685514819]
  We make efforts to facilitate research on CIS from three aspects. We formulate a pipeline for CIS with six sub-tasks: intent detection (ID), keyphrase extraction (KE), action prediction (AP), query selection (QS), passage selection (PS) and response generation (RG) We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS.
  arXiv  Detail & Related papers  (2021-05-18T06:35:36Z)
• Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
  The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
  arXiv  Detail & Related papers  (2021-01-19T12:23:32Z)
• Using a Binary Classification Model to Predict the Likelihood of Enrolment to the Undergraduate Program of a Philippine University [0.0]
  This study covered an analysis of various characteristics of freshmen applicants affecting their admission status in a Philippine university. A predictive model was developed using Logistic Regression to evaluate the probability that an admitted student will pursue to enroll in the Institution or not.
  arXiv  Detail & Related papers  (2020-10-26T06:58:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.