Student Academic Performance Prediction using Artificial Neural Networks: A Case Study
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This study presents a neural network model capable of predicting student’s GPA using students’ personal information, academic information, and place of residence to allow the institution to develop strategic programs that will help improve student performance and enable the student to graduate in time without any problem.Abstract:
Students dropout and delay in graduation are significant problems at Katsina State Institute of Technology and Management (KSITM). There are various reasons for that, students’ performances during first year is one of the major contributing factors. This study aims at predicting poor students’ performances that might lead to dropout or delay in graduation so as to allow the institution to develop strategic programs that will help improve student performance and enable the student to graduate in time without any problem. This study presents a neural network model capable of predicting student’s GPA using students’ personal information, academic information, and place of residence. A sample of 61 Computer Networking students’ dataset was used to train and test the model in WEKA software tool. The accuracy of the model was measured using well-known evaluation criteria. The model correctly predicts 73.68% of students’ performance and, specifically, 66.67% of students that are likely to dropout or experience delay before graduating.read more
Citations
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Educational data mining to predict students' academic performance: A survey study
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Ferian Fauzi Abdulloh,Majid Rahardi,A. Aminuddin,Sharazita Dyah Anggita,Arfan Yoga Aji Nugraha +4 more
TL;DR: In this article , the authors compared SMOTE, SMOTE-ENN, and SMOTETomek combined with SVM to detect the employability of graduates, with the highest accuracy and precision results produced by SMOTE SVM model by value of 0.96 and 0.89.
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Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University
TL;DR: The application of neural networks in predicting the students’ study period at the XYZ University uses a network model with GPA values as input and 1 layer of hidden layers with 10, 50 and 100 neurons; learning rate values used are 0.01, 0.1 and 0.3 for the study period.
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Predicting Students’ Academic Performance: A Review for the Attribute Used
Ismail Yusuf Panessai,Muhammad Modi Lakulu,Mohd Hishamuddin Abdul Rahman,Noor Anida Zaria Mohd Noor,Nur Iksan,Roznim Mohamad Rasli,Mohd Razimi Husin,Hishamuddin Ahmad,Shamsul Arrieya Ariffin,Asmara Alias,S. K. Subramaniam,Sanusi Majid,M. Ansyar Bora,Aldrin Aran Bilong,Nur Maisyara Mazli +14 more
TL;DR: In this article, a review on attributes, models and tools used to solve the problem of predicting students' academic performance is presented, based on the reference throughout 2009, the attributes used include demographic, academic grades, school related and social attributes.
References
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Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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A Review on Predicting Student's Performance Using Data Mining Techniques
TL;DR: An overview on the data mining techniques that have been used to predict students performance and how the prediction algorithm can be used to identify the most important attributes in a students data is provided.
Using data mining to predict secondary school student performance
TL;DR: The present work intends to approach student achievement in secondary education using BI/DM techniques, and shows that a good predictive accuracy can be achieved, provided that the first and/or second school period grades are available.
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A comparative analysis of machine learning techniques for student retention management
TL;DR: Analytical models to predict and to explain the reasons behind freshmen student attrition are developed and revealed that the educational and financial variables are among the most important predictors of the phenomenon.