Comparative study of supervised learning algorithms for student performance prediction

Mohammadi, Mehdi; Dawodi, Mursal; Wada, Tomohisa; Ahmadi, Nadira

doi:10.1109/icaiic.2019.8669085

Cited by 24 publications

(9 citation statements)

References 2 publications

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“…They also apply a binary genetic algorithm as a wrapper feature selection. In addition, Mohammadi et al (2019) apply three supervised learning algorithms (K-Nearest Neighbor, Naive Bayes, and Decision Tree) for student performance prediction. Masood et al (2019) implemented 11 machine learning models to predict student performance using two public student databases.…”

Section: Learning Analytics and Student Performance Predictionmentioning

confidence: 99%

A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance

2021

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Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background: Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student performance. Although the available sets of common prediction approaches, such as Artificial Neural Networks (ANN) and regression, work well with large datasets, they face challenges dealing with small sample sizes, limiting their practical applications in real practices. Methodology: Six distinct categories of performance antecedents are adopted here as course characteristics, LMS characteristics, student characteristics, student engagement, student support, and institutional factors, along with measurement items within each category. Furthermore, we assessed the student’s overall performance using three items of student satisfaction score, knowledge construction level, and student GPA. We have collected longitudinal data from 30 postgraduates in four subsequent semesters and analyzed data using the Learning Fuzzy Cognitive Map (LFCM) technique. Contribution: This research proposes a brand new approach, Learning Fuzzy Cognitive Map (LFCM), to predict student performance. Using this approach, we identified the most influential determinants of student performance, such as student engagement. Besides, this research depicts a model of interrelations among the student performance determinants. Findings: The results suggest that the model reasonably predicts the incoming sequence when there is a limited sample size. The results also reveal that students’ total online time and the regularity of learning interval in LMS have the largest effect on overall performance. The student engagement category also has the highest direct effect on student’s overall performance. Recommendations for Practitioners: Academic institutions can use the results and approach developed in this paper to identify students’ performance antecedents, predict the performance, and establish action plans to resolve the shortcomings in the long term. Instructors can adjust their learning methods based on the feedback from students in the short run on the operational level. Recommendation for Researchers: Researchers can use the proposed approach in this research to deal with the problems in other domains, such as using LMS for organizational/institutional education. Besides, they can focus on specific dimensions of the proposed model, such as exploring ways to boost student engagement in the learning process. Impact on Society: Our results revealed that students are at the center of the learning process. The degree to which they are dedicated to learning is the most crucial determinant of the learning outcome. Therefore, learners should consider this finding in order the gain value from the learning process. Future Research: As a potential for future works, the proposed approach could be used in other contexts to test its applicability. Future studies could also improve the performance level of the proposed LFMC model by tuning the model’s elements.

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Section: Learning Analytics and Student Performance Predictionmentioning

confidence: 99%

A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance

2021

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“…Modern data mining and machine learning techniques [19] are used for predicting student performance in small student cohorts. References [20] compare the effect of supervised learning algorithms for student performance prediction. References [21] build a decision tree-based algorithm, Logistic Model Trees (LMT) to learn the intrinsic relationship between the identified feature, which are identification of academic and socio-economic features, and students' academic grades.…”

Section: Related Workmentioning

confidence: 99%

Student Performance Prediction via Attention-Based Multi-Layer Long-Short Term Memory

Xie¹

2021

JCC

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Online education has attracted a large number of students in recent years, because it breaks through the limitations of time and space and makes high-quality education at your fingertips. The method of predicting student performance is to analyze and predict the student's final performance by collecting demographic data such as the student's gender, age, and highest education level, and clickstream data generated when students interact with VLE in different types of specific courses, which are widely used in online education platforms. This article proposes a model to predict student performance via Attention-based Multi-layer LSTM (AML), which combines student demographic data and clickstream data for comprehensive analysis. We hope that we can obtain a higher prediction accuracy as soon as possible to provide timely intervention. The results show that the proposed model can improve the accuracy of 0.52% -0.85% and the F1 score of 0.89% -2.30% on the four-class classification task as well as the accuracy of 0.15% -0.97% and the F1 score of 0.21% -2.77% on the binary classification task from week 5 to week 25.

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“…Naïve Bayes theorem determines the independence between the values of attributes. NB can be both predictive and descriptive algorithm which means, the probability is descriptive and then use to predict target is called a predictive algorithm [16].…”

Section: Naïve Bayes Classifiermentioning

confidence: 99%

IT Students Selection and Admission Analysis using Naïve Bayes and C4.5 Algorithm

Alejandrino¹

2020

IJATCSE

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Admission to college and selection of applications have probably become an integral part of some colleges and universities in their enrolment process, yet it is girded by controversy and skepticism. A new area of research that uses techniques of data mining is known as Educational Data Mining. It incorporates machine learning algorithms and statistical methods to help for the interpretation of student's learning habits, academic performances, and further improvements-if needed. This paper focuses on the predictive values of certain academic variables, admission tests, high school academic records as related to the performance of Information Technology (IT) students at the end of the first year. For this reason, 221 data were used, and C4.5 and Naive Bayes algorithms are applied to generate a prediction on the students' performance. The C4.5 classification gained 98.64% in 10-folds cross-validation and 96.97% in the 70% training and 30% testing percentage split compared to Naïve Bayes which only gained 89.14% and 86.36% for both 10-folds cross-validation and 70% training and 30% testing percentage split respectively. The comparative analysis of the result shows that senior high school track and academic data and admission test results are the influential attributes to the performance of IT students in their first year. This paper recommends for future studies to add different data from different years to increase the accuracy of the prediction.

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Comparative study of supervised learning algorithms for student performance prediction

Cited by 24 publications

References 2 publications

A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance

A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance

Student Performance Prediction via Attention-Based Multi-Layer Long-Short Term Memory

IT Students Selection and Admission Analysis using Naïve Bayes and C4.5 Algorithm

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