Predicting Academic Performance of Students from VLE Big Data using Deep Learning Models

Educational data mining has received considerable attention in the last few years. Many data mining techniques are proposed to extract the hidden knowledge from educational data. The extracted knowledge helps the institutions to improve their teaching methods and learning process. All these improvements lead to enhance the performance of the students and the overall educational outputs. In this paper, we propose a new student’s performance prediction model based on data mining techniques with new data attributes/features, which are called student’s behavioral features. These type of features are related to the learner’s interactivity with the e-learning management system. The performance of student’s predictive model is evaluated by set of classifiers, namely; Artificial Neural Network, Naive Bayesian and Decision tree. In addition, we applied ensemble methods to improve the performance of these classifiers. We used Bagging, Boosting and Random Forest (RF), which are the common ensemble methods used in the literature. The obtained results reveal that there is a strong relationship between learner’s behaviors and their academic achievement. The accuracy of the proposed model using behavioral features achieved up to 22.1% improvement comparing to the results when removing such features and it achieved up to 25.8% accuracy improvement using ensemble methods. By testing the model using newcomer students, the achieved accuracy is more than 80%. This result proves the reliability of the proposed model.

http://www.sersc.org/journals/IJDTA/vol9_no8/13.pdf

Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods

Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success, through which student attributes to focus on, up to which machine learning method is more appropriate to the given problem. This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.

https://link.springer.com/content/pdf/10.1186%2Fs41239-020-0177-7.pdf

Predicting academic success in higher education: literature review and best practices

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

https://dl.acm.org/doi/pdf/10.1145/3293881.3295783

Predicting academic performance: a systematic literature review

Educational Data Mining: A review of evaluation process in the e-learning

https://cyberleninka.org/article/n/566686.pdf

A Review on Predicting Student's Performance Using Data Mining Techniques

Huge amounts of data in educational datasets may cause the problem in producing quality data. Recently, data mining approach are increasingly used by educational data mining researchers for analyzing the data patterns. However, many research studies have concentrated on selecting suitable learning algorithms instead of performing feature selection process. As a result, these data has problem with computational complexity and spend longer computational time for classification. The main objective of this research is to provide an overview of feature selection techniques that have been used to analyze the most significant features. Then, this research will propose a framework to improve the quality of students’ dataset. The proposed framework uses filter and wrapper based technique to support prediction process in future study.

A proposed framework on hybrid feature selection techniques for handling high dimensional educational data

An epileptic seizure can be defined as a disturbance of consciousness, motor function, sensation, emotion, and behavior. The seizures can affect almost all aspects of life, such as lifestyle, health, education, and employment. Without a proper tool, epilepsy patient and caregiver finds difficulty in managing and monitoring epilepsy. The caregivers’ lifestyle is also affected when the patient strongly dependent on them. By using current technology, which is a mobile phone, MyEpiPal application will be implemented to solve these problems. MyEpiPal will act as a self-management and monitoring tool that can support the epilepsy patient and the caregiver. This application also will enable them to monitor side-effects and effectiveness of antiepileptic medicine, predict the possibility of seizure attack, and improve the quality of life. A fully functional mobile application will be a supportive tool for monitoring and managing epilepsy patient.

MyEpiPal: Mobile Application for Managing, Monitoring and Predicting Epilepsy Patient

The increasing number of the elderly population and process of modernization and migration for work has increase the issue of giving support for older people. This had contributed to a worrisome feeling among the caretakers. Therefore, Mobile Assistive Apps is designed to support and improve the quality of life among elderly. In order to develop the application, an empirical study had been conducted to discover the requirements for designing suitable Human Mobile Interaction (HMI) for elderly. Older people will experience cognitive, perceptual and psychomotor impairment that restricted them to use the smart phone or touch screen technology. This study had proposed a set of recommendations for designing HMI that will enhance their navigation and access to touch screen of the smart phone. The evaluation result with the average score 92% satisfaction on the interface design of the system indicated that the proposed recommendations had enhanced elderly navigation of the system. © 2015 Published by ISICO

/pdf/user-interface-design-for-elderly-mobile-assistive-systems-3ys883rfdj.pdf

User Interface Design for Elderly Mobile Assistive Systems

Students’ performance prediction can give a prior approximate knowledge of the students’ performance in future academic to the educators. However, it is not any easy task to perform prediction due to the poor identification of parameters and the lack of prediction techniques. In this paper, few parameters will be proposed and the most influenced parameters on students’ performance will be identified using chi squared. The hybrid of Decision Tree and Naive Bayes algorithms, NBTree will be used to classify the performance of new students. NBTree classifier undergoes the training and testing process using 10-folds cross validation technique and obtained the classification accuracy of 85.9 %, which is better than the accuracy of Decision Tree and Naive Bayes classifiers which are having 63.7 % and 72.6 % respectively. The classified performance result can be used by the educators to improve the teaching and learning process by developing new teaching methods and new teaching styles.

Amirah Mohamed Shahiri

Papers

A Review on Predicting Student's Performance Using Data Mining Techniques

A proposed framework on hybrid feature selection techniques for handling high dimensional educational data

MyEpiPal: Mobile Application for Managing, Monitoring and Predicting Epilepsy Patient

User Interface Design for Elderly Mobile Assistive Systems

An Exploratory Study on Students’ Performance Classification Using Hybrid of Decision Tree and Naïve Bayes Approaches