Advanced Prediction of Performance of a Student in an University using Machine Learning Techniques

Kumar, V. Uday; Krishna, Azmira; Neelakanteswara, P.; Basha, Cmak Zeelan

doi:10.1109/icesc48915.2020.9155557

Cited by 21 publications

(11 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In their research, Kumar et al (2020) assessed student performance in technical exams used for university application, an important process for universities. Forecasting whether students would pass or fail a technical exam based on their performance in certain subjects of their course was the technique used to approach the problem.…”

Section: Using the Naïve Bayes Algorithm For Forecasting Students' Ac...mentioning

confidence: 99%

“…In some cases, clustering is required for further data analysis. The aforementioned studies are aware of this fact because K-means is used in Kumar et al (2020) and Widyaningsih et al (2019), hierarchical in Kumar et al (2020), Daud et al (2017), andWidyaningsih et al (2019), and custom clustering types in Páez and Guzmán (2018) and others depending on the desired level of classification. Nevertheless, the most common and easiest to use is Naïve Bayes, which was used in Jayaprakash et al (2020), Widyaningsih et al (2019), and Amazona and Hernandez (2019).…”

Section: Using the Naïve Bayes Algorithm For Forecasting Students' Ac...mentioning

confidence: 99%

“…2.2. Using decision trees/random forests to predict student academic performance Kumar et al (2020) also proposed using the C5.0 algorithm, a decision tree algorithm, to predict student performance. For this, they used the exact same methodology as with Naïve Bayes.…”

Section: Using the Naïve Bayes Algorithm For Forecasting Students' Ac...mentioning

confidence: 99%

“…Of all the literature in this section, six authors used decision tree algorithms: Kumar et al (2020), Castrillón et al (2020), Daud et al (2017), Ma et al (2018), Hasan et al (2019), and Amazona and Hernandez (2019), while Jayaprakash et al (2020), Rincon-Flores et al (2020), andBenablo et al (2018) used random forest algorithms. Despite random forests being a subset of decision trees, most studies use decision trees for multiple-variable predictions, such as those for academic performance.…”

Section: Using the Naïve Bayes Algorithm For Forecasting Students' Ac...mentioning

confidence: 99%

See 3 more Smart Citations

Artificial neural network model to predict student performance using nonpersonal information

Chavez

Chavez-Arias²,

Contreras-Rosas³

et al. 2023

Front. Educ.

View full text Add to dashboard Cite

In recent years, artificial intelligence has played an important role in education, wherein one of the most commonly used applications is forecasting students’ academic performance based on personal information such as social status, income, address, etc. This study proposes and develops an artificial neural network model capable of determining whether a student will pass a certain class without using personal or sensitive information that may compromise student privacy. For model training, we used information regarding 32,000 students collected from The Open University of the United Kingdom, such as number of times they took the course, average number of evaluations, course pass rate, average use of virtual materials per date and number of clicks in virtual classrooms. Attributes selected for the model are as follows: 93.81% accuracy, 94.15% precision, 95.13% recall, and 94.64% F1-score. These results will help the student authorities to take measures to avoid withdrawal and underachievement.

show abstract

Section: Using the Naïve Bayes Algorithm For Forecasting Students' Ac...mentioning

confidence: 99%

Section: Using the Naïve Bayes Algorithm For Forecasting Students' Ac...mentioning

confidence: 99%

Section: Using the Naïve Bayes Algorithm For Forecasting Students' Ac...mentioning

confidence: 99%

Section: Using the Naïve Bayes Algorithm For Forecasting Students' Ac...mentioning

confidence: 99%

See 2 more Smart Citations

Artificial neural network model to predict student performance using nonpersonal information

Chavez

Chavez-Arias²,

Contreras-Rosas³

et al. 2023

Front. Educ.

View full text Add to dashboard Cite

show abstract

“…In [13], the authors have applied clustering and classification to evaluate the student performance and predict whether the student will pass or fail a technical exam. The data of students was obtained from Koneru Lakshmaiah (KL) University.…”

Section: On-campus Learning Environmentmentioning

confidence: 99%

Development of a Web-Based Prediction System for Students’ Academic Performance

Alboaneen

Almelihi

Alsubaie

et al. 2022

Data

View full text Add to dashboard Cite

Educational Data Mining (EDM) is used to extract and discover interesting patterns from educational institution datasets using Machine Learning (ML) algorithms. There is much academic information related to students available. Therefore, it is helpful to apply data mining to extract factors affecting students’ academic performance. In this paper, a web-based system for predicting academic performance and identifying students at risk of failure through academic and demographic factors is developed. The ML model is developed to predict the total score of a course at the early stages. Several ML algorithms are applied, namely: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Linear Regression (LR). This model applies to the data of female students of the Computer Science Department at Imam Abdulrahman bin Faisal University (IAU). The dataset contains 842 instances for 168 students. Moreover, the results showed that the prediction’s Mean Absolute Percentage Error (MAPE) reached 6.34%, and the academic factors had a higher impact on students’ academic performance than the demographic factors, the midterm exam score in the top. The developed web-based prediction system is available on an online server and can be used by tutors.

show abstract

A Systematic Study on Tertiary Level Student Tuition Fee Waiver Management During Pandemic Using Machine Learning Approaches

Shakir

Sutradhar

Sakib

et al. 2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Advanced Prediction of Performance of a Student in an University using Machine Learning Techniques

Cited by 21 publications

References 7 publications

Artificial neural network model to predict student performance using nonpersonal information

Artificial neural network model to predict student performance using nonpersonal information

Development of a Web-Based Prediction System for Students’ Academic Performance

A Systematic Study on Tertiary Level Student Tuition Fee Waiver Management During Pandemic Using Machine Learning Approaches

Contact Info

Product

Resources

About