Designing and evaluating a big data analytics approach for predicting students’ success factors

Fahd, Kiran; Miah, Shah J.

doi:10.1186/s40537-023-00835-z

Cited by 2 publications

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Predicting learning achievement using ensemble learning with result explanation

Tong,

2025

PLoS ONE

View full text Add to dashboard Cite

Predicting learning achievement is a crucial strategy to address high dropout rates. However, existing prediction models often exhibit biases, limiting their accuracy. Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education. To overcome these challenges, this research combines the strengths of various machine learning algorithms to design a robust model that performs well across multiple metrics, and uses interpretability analysis to elucidate the prediction results. This study introduces a predictive framework for learning achievement based on ensemble learning techniques. Specifically, six distinct machine learning models are utilized to establish a base learner, with logistic regression serving as the meta learner to construct an ensemble model for predicting learning achievement. The SHapley Additive exPlanation (SHAP) model is then employed to explain the prediction results. Through the experiments on XuetangX dataset, the effectiveness of the proposed model is verified. The proposed model outperforms traditional machine learning and deep learning model in terms of prediction accuracy. The results demonstrate that the ensemble learning-based predictive framework significantly outperforms traditional machine learning methods. Through feature importance analysis, the SHAP method enhances model interpretability and improves the reliability of the prediction results, enabling more personalized interventions to support students.

show abstract

Predicting learning achievement using ensemble learning with result explanation

Tong,

2025

PLoS ONE

View full text Add to dashboard Cite

show abstract

Role of AI in Academic Research

Kondaveeti,

Kumar,

Manusree

et al. 2024

Advances in Educational Technologies and Instructional Design

View full text Add to dashboard Cite

Artificial intelligence (AI) is rapidly transforming academic research, accelerating discovery, improving efficiency, and opening up new avenues across various disciplines. This chapter introduces AI and discusses its broad range of applications, both within and outside of academia. The chapter then presents the applications of AI in academic research. It covers various AI tools, such as Scite, Elicit, etc., and a comparative analysis of their capabilities and limitations. The chapter also discusses some ethical issues that must be considered when using AI in academic research. It concludes with a discussion on the future of AI in academic research and the potential benefits and challenges that lie ahead. This chapter is intended for a broad audience, including academic researchers, students, and those who are interested in learning more about the role of AI in academic research.

show abstract

Designing and evaluating a big data analytics approach for predicting students’ success factors

Cited by 2 publications

References 52 publications

Predicting learning achievement using ensemble learning with result explanation

Predicting learning achievement using ensemble learning with result explanation

Role of AI in Academic Research

Contact Info

Product

Resources

About