scholarly journals Implementing Machine Learning Techniques for Predicting Student Performance in an E-Learning Environment

2021 ◽  
Vol 4 (2) ◽  
pp. 149-156
Author(s):  
Adi Paramita
Keyword(s):  
Machine Learning ◽  
Learning Environment ◽  
Student Performance ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
E Learning ◽  
Predicting Student Performance

scholarly journals Student Learning Prediction Using Machine Learning Techniques

2019 ◽  
Vol 8 (6) ◽  
pp. 4179-4183
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Student Performance ◽  
Machine Learning Techniques ◽  
Data Mining Algorithms ◽  
Learning Techniques ◽  
Final Exams ◽  
E Learning ◽  
Using Data ◽  
Mining Algorithms

Now a day’s e-learning is smartly growing technology. This technology is more helpful for students to communicate with their professors through chats or emails. ELearning also removes the obstacle of physical presence of an Elearner. The main aim of this paper is to predict student performance in their final exams using different machine learning techniques. Information like attendance, marks, assignments, class participation, seminar, CA, projects and semester are collected to predict student performance. This prediction helps the instructors to analyze their students based on their performance. For that we have used WEKA tool for the prediction of the student performance. WEKA (Waikato Environment for Knowledge Analysis) is one of the data mining too which is used for the classification and clustering using data mining algorithms. This prediction helps the students and the staffs to know how much effort their students need to be put in their final exams to get good marks.

scholarly journals A Mining Frame Work of CO-PO Attainment using Deep Learning Techniques

2020 ◽  
Vol 9 (4S2) ◽  
pp. 60-63
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Answer Sheet ◽  
Input Image ◽  
Machine Learning Techniques ◽  
Optimal Model ◽  
Program Outcome ◽  
Learning Techniques ◽  
Frame Work ◽  
Machine Learning Models

student performance measured in CO-PO (Course Outcome and Program Outcome) attainment for OMR based answer sheet automation playing very curtail role in pupil concert analysis in this approach. In the proposed work, marks evaluation sheet is consider as input image, then apply frame cropping technique to extract the marks filled table by subdividing into cells as individual images by frame cropping technique. In order to recognition of hand written digit in each frame, various machine learning models are adopted, trained. Experimental results from proposed work show that convolutional neural network excels higher in identification digits from frames. The outputs are then converted to CSV version, which is used to evaluate CO-PO attainment for each learner. The experiments have been conducted and tested in proposed work on various machine learning techniques and compared the results to pick the optimal model

Dropout prediction in e-learning courses through the combination of machine learning techniques

2009 ◽  
Vol 53 (3) ◽  
pp. 950-965 ◽  
Author(s):  
Ioanna Lykourentzou ◽  
Ioannis Giannoukos ◽  
Vassilis Nikolopoulos ◽  
George Mpardis ◽  
Vassili Loumos
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
E Learning

The Impact of the ICT in the Analysis of Visual Attention Using Facial Expressions of the Students

2021 ◽  
pp. 185-199
Author(s):  
Muhammad Yasir Bilal ◽  
Rana Muhammad Amir Latif ◽  
N. Z. Jhanjhi ◽  
Mamoona Humayun
Keyword(s):  
Machine Learning ◽  
Visual Attention ◽  
Facial Expressions ◽  
Information Quality ◽  
Machine Learning Techniques ◽  
Brand Reputation ◽  
Learning Techniques ◽  
Information And Communication ◽  
E Learning ◽  
The Impact

Measuring and analyzing the student's visual attention are significant challenges in the e-learning environment. Machine learning techniques and multimedia tools can be used to examine the visual attention of a student. Emotions play a vital impact in understanding or judging the attention of the student in the class. If the student is interested in the lecture, the teacher can judge it by reading his emotions, and the learning has increased, and students can pay more attention to the classroom, authors say. The study explores the effect on the brand reputation of universities of information and communication technology (ICT), e-service quality, and e-information quality by focusing on the e-learning and fulfillment of students.

Role of Machine Learning in Modern Education and Teaching

2021 ◽  
pp. 99-123
Author(s):  
Latika Kharb ◽  
Prateek Singh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Student Performance ◽  
Student Perception ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Improve Student Performance ◽  
Student Models ◽  
Performance Teachers ◽  
The Impact

Computers are being utilized in field in education for many years. In last few decades, research within the field of artificial intelligence (AI) is positively affecting educational application. Advanced machine learning and deep learning techniques could be used for extracting knowledgeable information from crude information. In this chapter, the authors have analysed the impact of artificial intelligence in the education domain. The authors will discuss how with the development of machine learning techniques in last few decades, machine learning models can anticipate student performance. By learning about every student, models can identify the shortcomings. Then the authors will propose different approaches to improve student performance. Teachers can also use this model to understand student perception levels in a better way so that they can modulate their lectures according to student perception levels.

scholarly journals Student Intervention System using Machine Learning Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2061-2065
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Student Records ◽  
Teaching Methodologies ◽  
Machine Learning Classification ◽  
Classification Technique ◽  
Learning Techniques ◽  
Vector Machines

Now a days, the educational institutes are adopting technologies for betterment of student’s quality, in respect to teaching methodologies etc. For which the huge information available with educational institutes can be used to predict student’s future in academics. The main objective of this paper is to predict the student performance in the examination and also to predict the student will graduate or not. Hence forth we are using statistical analytical method which is F1 score. F1 score or F measure is used to test the prediction accuracy by considering precision and recall to compute the score. To fulfill this requirement in machine learning, classification technique is used. The dataset used in this analysis contains 395 student records, having attributes, such as age, health, internet, school, father job, mother job etc. Using support vector machines (SVM), Decision Tree and Naïve Bayes (NB) classification algorithms F1 score is calculated for each algorithm. Based on the analysis done the F1 score of support vector machine is giving the better prediction compared to rest of the two algorithms.

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