Prediction of Student's Performance Using Machine Learning

Chauhan, Nupur; Shah, Kimaya; Karn, Divya; Dalal, Jignasha

doi:10.2139/ssrn.3370802

Cited by 13 publications

(9 citation statements)

References 6 publications

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“…Figure 6 highlights the distribution of the reviewed studies according to the sample population used. While most researchers took student-centred approaches, with nearly half of the studies (47%) focusing on undergraduate students, very few considered the inclusion of educators/teachers in their predictive models, precisely three (3) articles mentioned this [48], [95], [118]. These findings introduce a possible research gap for future researchers to develop a more comprehensive prediction model.…”

Section: B Dataset Sample Population Size and Collection Methodsmentioning

confidence: 99%

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

et al. 2022

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Student retention is an essential measurement metric in education, indicated by retention rates, which are accumulated as students re-enroll from one academic year to the next. High retention rates can be obtained if institutions aim to provide appropriate support and teaching methods among the various practices to prevent students from deferring their studies. To address this pressing challenge faced by educational institutions, the underlying factors and the methodological aspects of building robust predictive models are reviewed and scrutinized. Educational Data Mining (EDM) and Learning Analytics (LA) have been widely adopted for knowledge discovery from educational data sources, improving the teaching practice, and identifying at-risk students. Various predictive techniques are applied in LA, such as Machine Learning (ML), Statistical Analysis, and Deep Learning (DL). To gain an in-depth review of these techniques, academic publications have been reviewed to highlight their potential to resolve Student Retention issues in education. Additionally, the paper presents a taxonomy of ML approaches and a comprehensive review of the success factors and the features that are not indicative of student performance in three different learning environments: Traditional Learning, Blended Learning, and Online Learning. The survey reveals that supervised ML and DL techniques are broadly applied in Student Retention. However, the application of ensemble and unsupervised learning clustering techniques supporting the heterogenous and homogenous groups of students is generally lacking. Moreover, static and traditional features are commonly used in student performance, ignoring vital factors such as educators-related, cognitive, and personal data. Furthermore, the paper highlights open challenges for future research directions.

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Section: B Dataset Sample Population Size and Collection Methodsmentioning

confidence: 99%

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

et al. 2022

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“…In [12], the authors developed a regression model to predict the score that a student would have, used the ALGORITHM KNN, Decision Tree, SVM, Random Forest and Multiple Linear Regression. After comparing the results of each algorithm, it was the Multiple Linear Regression Model that obtained the greatest accuracy.…”

Section: Related Workmentioning

confidence: 99%

Predicting Academic Performance using a Multiclassification Model: Case Study

Vergaray¹,

Guerra²,

Cervera³

et al. 2022

IJACSA

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Now-a-days predicting the academic performance of students is increasingly possible, thanks to the constant use of computer systems that store a large amount of student information. Machine learning uses this information to achieve big goals, such as predicting whether or not a student will pass a course. The main purpose of the work was to make a multiclassifier model that exceeds the results obtained from the machine learning models used independently. For the development of our proposed predictive model, the methodology was used, which consists of several phases. For the first step, 557 records with 25 characteristics related to academic performance were selected, then the preprocessing was applied to said data set, eliminating the attributes with the lowest correlation and those records with inconsistencies, leaving 500 records and 9 attributes. For the transformation, it was necessary to convert categorical to numerical data of four attributes, being the following: SEX, ESTATUS_lab_padre, ESTATUS_lab_madre and CONDITION. Having the data set clean, we proceeded to balance the data, where 1,167 data were generated, using the 2/3 for training and the remaining 1/3 for validation, then the following techniques were applied: Extra Tree, Random Forest, Decision Tree, Ada Boost and XGBoost, each obtained an accuracy of 57.41%, 61.96%, 91.44%, 59.65% and 83.3% respectively. Then the proposed model was applied, combining the five algorithms mentioned above, which reached an accuracy of 92.86%, concluding that the proposed model provides better accuracy than when the models are used independently meaning that it was the one that obtained the best result.

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“…Several algorithms were chosen and evaluated to in deploying a reliable and accurate prediction. Predictive modelling used to predict plant disease is related to several machine learning tasks, such as classification, regression and clustering [ 102 ]. To predict whether a plant is healthy or infected, disease prediction of plants based on a classification technique should be applied on the spectral data.…”

Section: Machine Learning Techniques For Plant Disease Predictionmentioning

confidence: 99%

Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Tan

Jamlos

Omar

et al. 2021

Sensors

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Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

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Prediction of Student's Performance Using Machine Learning

Cited by 13 publications

References 6 publications

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

Predicting Academic Performance using a Multiclassification Model: Case Study

Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

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