Predicting Student Enrolments and Attrition Patterns in Higher Educational Institutions using Machine Learning

Shilbayeh, Samar; Abonamah, Abdullah A.

doi:10.34028/18/4/8

Cited by 13 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Additionally, they propose that these methods can be applied more broadly to benefit other higher education institutions, aiding in enhancing their overall performance and sustainability. The boosted regression tree model for predicting student enrolments achieved 89% accuracy using 10-fold crossvalidation and outperformed the single regression tree model that achieved only 76% accuracy (Shilbayeh & Abonamah, 2021).…”

Section: Machine Learning Approachmentioning

confidence: 97%

Enhancing Business Sustainability Through Technology-Enabled AI: Forecasting Student Data and Comparing Prediction Models for Higher Education Institutions (HEIs)

Gnoh,

Keoy,

Iqbal

et al. 2024

PaperASIA

View full text Add to dashboard Cite

This study aims to enhance business sustainability in the context of Higher Education Institutions (HEIs) by utilizing AI and forecasting techniques. It explores the development and comparison of prediction models, including the use of dashboard development, to support decision-making processes within HEIs. The study covers various aspects, including the background of forecasting and prediction models, the use of specific models such as the Prophet Model, Long Short-Term Memory (LSTM) Model, and Polynomial Regression Model, as well as the importance of dashboards for HEIs. The methodology section outlines the data collection and preparation process, model selection, approach, diagrams, functional and non-functional requirements, justification of tools, and libraries and models used. The implementation section delves into the system design and development of the dashboard, including the login page, homepage, forecast page, and insert data page. As for the findings, the LSTM Model has proven to be the most accurate and suitable model to be implemented for forecasting student enrolment data in this study. The dashboard's future enhancements involve adding more faculties, predictive features for resource allocation, refining the visual identity, improving user registration on the login page, and exploring better models for student enrolment predictions. Overall, the study provides valuable insights into the application of AI and forecasting techniques in HEIs, aiming to enhance business sustainability and decision-making processes. It contributes to the growing body of knowledge on the use of technology-enabled AI in higher education institutions, with a focus on forecasting student enrolment data and developing prediction models.

show abstract

Section: Machine Learning Approachmentioning

confidence: 97%

Enhancing Business Sustainability Through Technology-Enabled AI: Forecasting Student Data and Comparing Prediction Models for Higher Education Institutions (HEIs)

Gnoh,

Keoy,

Iqbal

et al. 2024

PaperASIA

View full text Add to dashboard Cite

show abstract

“…Understanding existing gender differences in skill development is becoming increasingly important to gain valuable information on how to close the gender gap. The analysis of specific sociodemographic characteristics in machine learning studies in education is still in its infancy [48], [49]. Gender prediction using machine learning models mainly focused on gender prediction of educational leadership [50]; female models and reinforcement in STEM [51]; exploring gender differences in learning computational thinking [52]; the intersection of the academic gender gap [53]; and gender stereotyping in academic dropout [54].…”

Section: Gender In Educationmentioning

confidence: 99%

Forecasting Gender in Open Education Competencies: A Machine Learning Approach

Ibarra-Vazquez,

Ramírez-Montoya,

Buenestado-Fernández

2024

IEEE Trans. Learning Technol.

View full text Add to dashboard Cite

This article aims to study the performance of machine learning models in forecasting gender based on the students' open education competency perception. Data were collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. The analysis comprises 1) a study of the students' perceptions of knowledge, skills, and attitudes or values related to open education and its sub-competencies from a 30-item questionnaire using machine learning models to forecast participants' gender, 2) validation of performance through cross-validation methods, 3) statistical analysis to find significant differences between machine learning models, and 4) an analysis from explainable machine learning models to find relevant features to forecast gender. The results confirm our hypothesis that the performance of machine learning models can effectively forecast gender based on the student's perceptions of knowledge, skills, and attitudes or values related to open education competency.

show abstract

“…An AI virtual assistant at the University of Georgia answered more than 5,000 admissions questions, saving staff time [25]. Operationally, AI can predict student attrition, pattern enrolment, improve building efficiency, and improve campus security through pattern recognition in security footage [29]. The benefits of AI are presented as a table 1.…”

Section: The Transformative Potential Of Ai In Higher Educationmentioning

confidence: 99%

Strategic leadership for responsible artificial intelligence adoption in higher education

Tarisayi

2024

CTE Workshop Proc.

View full text Add to dashboard Cite

Artificial intelligence (AI) has the potential to transform higher education through its potential to improve learning, research, and leadership in colleges and universities. This study aims to identify key themes, challenges, and recommendations related to AI implementation in higher education and leadership. A literature review was supported by 2 LLMs. The results show that AI is being used to personalize teaching, provide formative feedback, identify at-risk students, accelerate research discovery, streamline administrative processes through chatbots, and optimize resource utilization. However, the findings also highlight significant technical, ethical, cultural and resource challenges that institutions must overcome in order to successfully harness AI's enormous potential while mitigating risks. Empowering management through knowledge, funding, and support structures that promote good governance and responsible AI adoption is fundamental to successfully harnessing the enormous but still largely untapped potential of AI.

show abstract

Predicting Student Enrolments and Attrition Patterns in Higher Educational Institutions using Machine Learning

Cited by 13 publications

References 2 publications

Enhancing Business Sustainability Through Technology-Enabled AI: Forecasting Student Data and Comparing Prediction Models for Higher Education Institutions (HEIs)

Enhancing Business Sustainability Through Technology-Enabled AI: Forecasting Student Data and Comparing Prediction Models for Higher Education Institutions (HEIs)

Forecasting Gender in Open Education Competencies: A Machine Learning Approach

Strategic leadership for responsible artificial intelligence adoption in higher education

Contact Info

Product

Resources

About