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Framework for suggesting corrective actions to help students intended at risk of low performance based on experimental study of college students using explainable machine learning model

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Abstract

Today, the main aim of educational institutes is to provide a high level of education to students, as career selection is one of the most important and quite difficult decisions for learners, so it is essential to examine students' capabilities and interests. Higher education institutions frequently face higher dropout rates, low academic achievement, and graduation delays. One potential answer to these issues is to better leverage student data stored in institutional databases and online learning platforms to forecast students' academic achievements early by using artificial intelligence and advanced computer algorithms. Several research projects have been launched with the goal of building systems that can predict student performance. However, a system that can forecast student performance and identify the various factors that directly impact it is required. The purpose of this research work is to create a model that correctly identifies students who are in danger of low performance, as well as to identify the factors that contribute to this phenomenon and suggesting the remedial actions so as to reduce dropout rate and low performance among students. The emphasis of this study is to explore various factors that may affect mental health which lead to low performance or loss of interest in studies. The developed model can accurately identify at-risk students with over 96.5% accuracy using Machine learning techniques. This study focuses extensively on various factors apart from academics, such as personal and family factors and their association with student performance. To increase the accuracy of performance predictions, the model combines explainable Machine learning techniques to outline the factors associated with poor performance and discusses a novel framework that will help to increase the accuracy of prediction of the established prediction system. This assists low-performing students in improving their academic metrics by executing corrective actions that address the issues. The proposed novel framework, with the help of a mapping table, will recommend corrective actions along with visualization using the heatmap technique which may help the students to perform better in exams, increase the institution's effectiveness, and improves any country’s economic growth and stability.

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Data availability

I declare that data collected by authors for purpose of study are original & data of this study “Stu_perf” is available from correspondence author upon reasonable request.

Abbreviations

ML:

Machine learning

AI:

Artificial Intelligence

RA:

Remedial Actions

EQ:

Equations

AUC:

Area under the curve

EDM:

Educational data mining

CSV:

Comma-separated value

ITS:

Intelligent tutoring systems

LMS:

Learning management systems

TG/TS:

Target grade/Target score

XGB:

EXtreme gradient boosting

LGB:

Light gradient boosting

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Authors and Affiliations

  1. Department of Computer Science, Guru Gobind Singh Indraprastha University, New Delhi, India

    Harsimran Singh

  2. Department of Information & Communication Technology, Indira Gandhi Delhi Technical University for Women, Delhi, India

    Banipreet Kaur & Arun Sharma

  3. Department of Information Technology, Deakin University, Geelong, Australia

    Ajeet Singh

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  2. Banipreet Kaur
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Conceptualization, methodology, software, statistical analysis, writing—original draft preparation, Data curation, writing—review and editing, visualization, Data analysis, Investigation, literature review:

Harsimran Singh1, Banipreet Kaur2.

Resources Tools: Ajeet Singh4.

Supervision: Arun Sharma2; Discussion: all authors.

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Correspondence to Harsimran Singh.

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Singh, H., Kaur, B., Sharma, A. et al. Framework for suggesting corrective actions to help students intended at risk of low performance based on experimental study of college students using explainable machine learning model. Educ Inf Technol 29, 7997–8034 (2024). https://doi.org/10.1007/s10639-023-12072-1

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