A Machine Learning Approach to Predictive Modelling of Student Performance

Ng, Hu; Azha, Azmin Alias bin Mohd; Yap, Timothy Tzen Vun; Goh, Vik Tor

doi:10.12688/f1000research.73180.1

Cited by 6 publications

(6 citation statements)

References 9 publications

(9 reference statements)

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“…When exploring other studies where machine learning was used to explore absenteeism, we found a few examining the relationship between asthma and absenteeism (Lary et al, 2019) predictive modeling of student performance (Ng et al, 2021), and attendance autistic students (Jarbou et al, 2022). More is surely available, but these give a glimpse into many types of opportunities for exploration using this method.…”

Section: Machine Learningmentioning

confidence: 99%

The Unlearning of School Attendance: Ideas for Change

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Machine Learningmentioning

confidence: 99%

The Unlearning of School Attendance: Ideas for Change

2024

Frontiers Research Topics

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“…Recently, Ng et al [7] have proposed a data mining approach for identifying essential factors that affect student performance based on data from two secondary schools in Portugal. Several machine learning algorithms are used for classification: Support Vector Machine (SVM), NB, and Multilayer Perceptron (MLP).…”

Section: Related Workmentioning

confidence: 99%

“…Another study [7] used the same dataset with three classification models: SVM, NB, and MLP. The performance indicators were F1-Score, recall, accuracy, and precision.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network for Predicting Student Academic Performance in Intelligent Tutoring System

Alshaikh,

Hewahi

2024

IJCDS

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One of the most significant research areas in education and Artificial Intelligence (AI) is the earlier prediction of students' academic achievement. Limited studies have been conducted using Deep Learning (DL) in the student domain of Intelligent Tutoring System (ITS). Traditional Machine Learning (ML) techniques have been employed in many earlier publications to predict student performance. This paper investigates the effectiveness of DL algorithms for predicting student academic performance. Three different DL architectures based on the structure of Convolutional Neural Networks (CNN) are presented. Two public datasets are used. Furthermore, two feature selection techniques are utilized in this experiment: Principal Component Analysis (PCA) and Decision Trees (DTs). Also, we applied a resampling technique for the first dataset to address the issue of an imbalanced dataset. According to the experimental findings, the proposed CNN model's success in predicting student performance at early stages reached an accuracy of 94.36% using the first dataset and 84.83% using the second dataset. Comparing the proposed approach with the previous studies, the proposed approach outperformed all previous studies when dataset 2 and part of dataset 1 were used. For the complete dataset 1, the proposed model performed very well.

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“…When exploring other studies where machine learning was used to explore absenteeism, we found a few examining the relationship between asthma and absenteeism ( Lary et al, 2019 ) predictive modeling of student performance ( Ng et al, 2021 ), and attendance autistic students ( Jarbou et al, 2022 ). More is surely available, but these give a glimpse into many types of opportunities for exploration using this method.…”

Section: Literature Reviewmentioning

confidence: 99%

Revealing underlying factors of absenteeism: A machine learning approach

et al. 2022

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IntroductionThe basis of support is understanding. In machine learning, understanding happens through assimilated knowledge and is centered on six pillars: big data, data volume, value, variety, velocity, and veracity. This study analyzes school attendance problems (SAP), which encompasses its legal statutes, school codes, students’ attendance behaviors, and interventions in a school environment. The support pillars include attention to the physical classroom, school climate, and personal underlying factors impeding engagement, from which socio-emotional factors are often the primary drivers.MethodsThis study asked the following research question: What can we learn about specific underlying factors of absenteeism using machine learning approaches? Data were retrieved from one school system available through the proprietary Building Dreams (BD) platform, owned by the Fight for Life Foundation (FFLF), whose mission is to support youth in underserved communities. The BD platform, licensed to K-12 schools, collects student-level data reported by educators on core values associated with in-class participation (a reported—negative or positive—behavior relative to the core values) based on Social–Emotional Learning (SEL) principles. We used a multi-phased approach leveraging several machine learning techniques (clustering, qualitative analysis, classification, and refinement of supervised and unsupervised learning). Unsupervised technique was employed to explore strong boundaries separating students using unlabeled data.ResultsFrom over 20,000 recorded behaviors, we were able to train a classifier with 90.2% accuracy and uncovered a major underlying factor directly affecting absenteeism: the importance of peer relationships. This is an important finding and provides data-driven support for the fundamental idea that peer relationships are a critical factor affecting absenteeism.DiscussionThe reported results provide a clear evidence that implementing socio-emotional learning components within a curriculum can improve absenteeism by targeting a root cause. Such knowledge can drive impactful policy and programming changes necessary for supporting the youth in communities overwhelmed with adversities.

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A Machine Learning Approach to Predictive Modelling of Student Performance

Cited by 6 publications

References 9 publications

The Unlearning of School Attendance: Ideas for Change

The Unlearning of School Attendance: Ideas for Change

Convolutional Neural Network for Predicting Student Academic Performance in Intelligent Tutoring System

Revealing underlying factors of absenteeism: A machine learning approach

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