Student Result Prediction in Covid-19 Lockdown using Machine Learning Techniques

Jeslet, D. Santhi; Komarasamy, Dinesh; Hermina, J. Jenita

doi:10.1088/1742-6596/1911/1/012008

Cited by 3 publications

(3 citation statements)

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“…Figure 7 presents a bar of the frequency of studies based on the sample size. It is observed that only twenty-six (26) studies used a dataset with more than or equal to 1000 instances to train the predictive models ([13], [16], [58], [65], [68], [69], [80], [89], [90], [99], [107], [112], [27], [119]- [121], [123], [127], [129], [45], [46], [50], [51], [53], [55], [56]). However, it is known that most Machine Learning algorithms require a massive amount of data to perform well and produce accurate prediction results.…”

Section: B Dataset Sample Population Size and Collection Methodsmentioning

confidence: 99%

“…The second highest is IBM SPSS for statistical approaches with 13% ( [13], [66], [70], [82]- [84], [86], [95], [120], [136]). Other most-used tools include Microsoft Excel (17%) ([20], [52], [117], [123], [135], [71], [82]- [84], [86], [89], [112], [115]), RapidMiner Studio (9%) ( [13], [49], [70], [86], [91], [107], [131]), Jupyter Notebook (7%) ( [115], [123], [124], [127], [132]), Orange and R Studio (4%) ( [62], [87], [116], [125], [128], [133] respectively), and Tableau (3%) ( [77], [127]).…”

Section: Prediction Approaches Algorithms Learning Outcome Type and T...mentioning

confidence: 99%

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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%

Section: Prediction Approaches Algorithms Learning Outcome Type and T...mentioning

confidence: 99%

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

et al. 2022

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“…The smart education model, which takes advantage of information technology such as big data analysis and artificial intelligence, has become a trend for new types of education. By analyzing students' interests, hobbies, habits and other behaviours, it has a significant impact on their personal development and academic performance [3]. The key to predicting student behaviour is an understanding of behavioural patterns in students' habits.…”

Section: Introductionmentioning

confidence: 99%

SCAN Algorithm to Optimize Dynamic Instructional Information Network for Predicting Student Behavior

Teng

2024

SCPE

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With the integration of work, study and life on campus taking shape gradually in major universities across China, the concept of education from classroom to life is more widely accepted and the smart education model has become a trend in education informatization. However, most of the existing studies are not applicable to student behaviour data in the campus environment, and the temporal as well as cyclical characteristics in the data are not accessible in the student behaviour prediction problem. In this study, a hypergraph-based dynamic campus behaviour information network is designed to address the needs of the student behaviour prediction problem, and a student campus behaviour prediction algorithm is proposed in the dynamic campus behaviour information network-based student behaviour prediction problem. The effectiveness and rationality of the algorithm is verified through experiments with real campus data sets. The experimental results demonstrated that the periodic nature of the data acquired by the cycle gated cyclic unit module needs to be built on top of the snapshot gated cyclic unit module to help the algorithm achieve better results. The area under the curve of the algorithm proposed in the study achieves more advantageous results on both the 21-day and 35-day datasets. The cycle gated cyclic unit module of the student campus behaviour prediction algorithm proposed in this study can more effectively extract the cyclic features present in the dynamic campus behaviour information network and better accomplish the prediction of student campus behaviour.

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Assessing The Importance of Dynamic Features of Academic Performance Using Machine Learning Models During Distance Education Caused By COVID-19

Yunusov,

Gafarov,

Gilemzyanov

et al. 2023

2023 5th International Conference on Cybernetics and Intelligent System (ICORIS)

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Student Result Prediction in Covid-19 Lockdown using Machine Learning Techniques

Cited by 3 publications

References 1 publication

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

SCAN Algorithm to Optimize Dynamic Instructional Information Network for Predicting Student Behavior

Assessing The Importance of Dynamic Features of Academic Performance Using Machine Learning Models During Distance Education Caused By COVID-19

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