Student pass rates prediction using optimized support vector machine and decision tree

Ma, Xiaofeng; Zhou, Zhurong

doi:10.1109/ccwc.2018.8301756

Cited by 29 publications

(21 citation statements)

References 7 publications

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“…Next, SVM scored the second highest of the prediction accuracy which is 93.95%. The reason why SVM worked well is that it is able to solve high-dimensional data which is data that associates with many attributes and features [22]. SVM also can perform efficiently, with only small tuning.…”

Section: Discussionmentioning

confidence: 99%

“…SVM constructs hyperplanes in multidimensional space to perform its classification, which separates class levels into different cases. [22] concerns with the importance of enhancing student pass rates as it reflects the school teaching level. Therefore, they have analysed the prominent features of students and forecasted their pass rate using SVM and DT.…”

Section: ) Support Vector Machinementioning

confidence: 99%

See 1 more Smart Citation

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

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Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students' achievement, university's performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students' academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students' graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naï ve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students' graduation time.

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Section: Discussionmentioning

confidence: 99%

Section: ) Support Vector Machinementioning

confidence: 99%

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

View full text Add to dashboard Cite

show abstract

“…The Tensor flow deep learning model is optimally configured to achieve the highest prediction accuracy. Ma et al [2] considered the dependency among student's attributes to initialize coefficients of machine learning algorithms using initialization coefficients rules. This helps in faster convergence of algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It expects that the presence of an unambiguous aspect of a class is autonomous of every other aspect. As per Bayes theorem, the contingent probability is given by the Equations ( 1) and (2). It is the most successful algorithm for many applications such as text document classification, spam filtering, Recommender system, etc.…”

Section: Naïve Bayes Classificationmentioning

confidence: 99%

Student Classification Based on Cognitive Abilities and Predicting Learning Performances Using Machine Learning Models

Vital¹,

Sangeeta²,

Kumar³

2021

IJCDS

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“…SVM comes in second for the number of highest accuracy rates achieved in this study. 5 over 19 papers used SVM to predict academic success [13], academic performance [19,34,45,50] and students' pass rate [35]. For details on the accuracy rates achieved and feature categories used in these studies, refer Table 6.…”

Section: 32support Vector Machine (Svm)mentioning

confidence: 99%

A systematic literature review on student performance predictions

Nawang¹,

Makhtar²,

Hamza³

2021

IJATEE

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Nowadays, many high school and higher educational systems generate a large number of student information through the learning management system, examination data, students' activities, library system, etc. [1]. This situation leads to increases in the volume and types of educational data in every institution. Machine learning (ML), learning analytics (LA), and data mining (DM) approaches have been widely used on educational data to predict students' performance. These approaches have shown that several techniques and algorithms are useful in understanding this domain, which is poorly accessed by human capability.Students' success has become an important metric to higher educational institutes as well as secondary level schools. In higher education institutions, students' performance plays a vital role in determining their job success [2]. Good academic performance assures employers of a candidate's quality and reliability.

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Student pass rates prediction using optimized support vector machine and decision tree

Cited by 29 publications

References 7 publications

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Student Classification Based on Cognitive Abilities and Predicting Learning Performances Using Machine Learning Models

A systematic literature review on student performance predictions

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