Student performance predictor using multiclass support vector classification algorithm

Athani, Suhas; Kodli, Sharath A; Banavasi, Mayur N; Hiremath, P. S.

doi:10.1109/cspc.2017.8305866

Cited by 20 publications

(10 citation statements)

References 12 publications

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“…Through the systematic review that has been made in this study, it is found that the methods most often used by previous researchers can be categorized into three, namely classification, cluster and regression. However, there are recent methods that have incorporated different fields into their methods such as data mining as in [13], [30], [31]. Data mining is a method that combines methods between machine learning, statistics, and database systems to identify patterns in large data sets.…”

Section: Predictive Models Used In Previous Studies and Resultsmentioning

confidence: 99%

Systematic Research on Predictive Models on Students’ Academic Performance in Higher Education

2019

ijrte

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In Malaysia, the students’ academic assessment has changed over time as it is now made to be more holistic. Predictive models used for educational data in Malaysia are still inadequate in giving a true picture of the students’ academic performance. The reason behind this is due to the lack of research in Malaysian context on predictive models and factors affecting students' academic achievement. Therefore, this study will conduct a systematic review on the literature, in order to identify the predictive methods for students’ academic performance in higher education. Through three major bases: WoS, Science Direct and IEEE Xplore, an extensive search was conducted to find any related articles from 2014 to 2018 that use "predict", "forecast", "academic performance", "student" and "higher education" keywords in their text. Initially, 195 articles were selected to be used in this review. However, through titles and abstracts’ filtering and screen processing, only 69 articles found to discuss on the predictive model for student's academic performance at higher education level. Based on extensive reading, the most widely used attributes in the predictive models is the academic process. While predictive models can be categorized into three, namely classification, cluster and regression, there are nine methods used by previous researchers in all categories. The most widely used category from previous studies was the classification, with 33 articles. This study has listed the advantages and disadvantages of each method based on the previous studies. This study also has identified the challenges and gaps faced by previous researchers in improving the existing models in the future. Among the challenges faced by the predictive models are the amount of data and assumptions that need to be followed before analysis can be made. In the future, the predictive models used for students’ academic performance should consider the latest assessments’ valuation method based on a modern educational system that emphasizes on soft skills, interpersonal skills and high-level thinking capabilities.

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Section: Predictive Models Used In Previous Studies and Resultsmentioning

confidence: 99%

Systematic Research on Predictive Models on Students’ Academic Performance in Higher Education

2019

ijrte

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“…They found that the RF and DL algorithms are suitable for predicting bene cial sessions during e learning, and they reached from the prediction results to factors that affect the effectiveness of sessions such as family commitment, study environment and teaching style. (Athani et al, 2017), proposed a prediction system using SVM algorithm. The results showed that SVM algorithm used to predict the performance of students and provide the departments of the institution, information about the status of students, and thus provide students with appropriate additional educational tasks that help them improve their academic performance.…”

Section: Related Workmentioning

confidence: 99%

Student Performance Prediction Using Machine Learning Techniques

El‐Hafeez

Ahmed

2022

Preprint

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With the emergence of the covid 19 pandemic, E-learning usage was the only way to solve the problem of study interruption in educational institutions and universities. Therefore, this field has garnered significant attention in recent times. In this paper, we used ten machine-learning algorithms (Logistic Regression, Decision Tree, Random Forest, SGD Classifier, Multinomial NB, K-Neighbors Classifier, Ridge Classifier, Nearest Centroid, Complement NB and Bernoulli NB) to build a prediction system based on artificial intelligence techniques to predict the difficulties students face in using the e-learning management system, and support related decision-making. Which, in turn, contributes to supporting the sustainable development of technology at the university. From the results obtained, we found the important factors that affect the use of E-learning to solve students' learning difficulties by using LMS.

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“…The performance of students can be evaluated from various aspects. Several studies evaluated students in general in terms of student performance [18][19][20][21][22] and some other studies evaluated students for a specific purpose such as academic achievement [23,24], reading ability [25,26], grading [27][28][29], dropout prediction [30][31][32][33], etc. Below, some state-of-the-art research studies have been discussed for each of the mentioned tasks that evaluated the student performance from different aspects.…”

Section: Related Workmentioning

confidence: 99%

“…In this research, the usefulness of ML was highlighted for monitoring student performance. In ref [18], student performance was predicted using multi-class support vector classification. Real data from 395 secondary students in Portugal were employed, which was collected by a questionnaire method and school reports.…”

Section: Evaluation Of Student Performancementioning

confidence: 99%

A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning

et al. 2021

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The objective of this research is to develop an machine learning (ML) -based system that evaluates the performance of high school students during the semester and identify the most significant factors affecting student performance. It also specifies how the performance of models is affected when models run on data that only include the most important features. Classifiers employed for the system include random forest (RF), support vector machines (SVM), logistic regression (LR) and artificial neural network (ANN) techniques. Moreover, the Boruta algorithm was used to calculate the importance of features. The dataset includes behavioral information, individual information and the scores of students that were collected from teachers and a one-by-one survey through an online questionnaire. As a result, the effective features of the database were identified, and the least important features were eliminated from the dataset. The ANN accuracy, which was the best accuracy in the original dataset, was reduced in the decreased dataset. On the contrary, SVM performance was improved, which had the highest accuracy among other models, with 0.78. Moreover, the LR and RF models could provide the same performance in the decreased dataset. The results showed that ML models are influential for evaluating students, and stakeholders can use the identified effective factors to improve education.

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Student performance predictor using multiclass support vector classification algorithm

Cited by 20 publications

References 12 publications

Systematic Research on Predictive Models on Students’ Academic Performance in Higher Education

Systematic Research on Predictive Models on Students’ Academic Performance in Higher Education

Student Performance Prediction Using Machine Learning Techniques

A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning

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