Learning Style Detection in E-learning Systems Using Machine Learning Techniques

2021 ◽  
pp. 114774
Author(s):  
Fareeha Rasheed ◽  
Abdul Wahid
Keyword(s):  
Machine Learning ◽  
Learning Style ◽  
Learning Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
E Learning

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

scholarly journals EXPLORING THE STACKING STATE-SPACE

2002 ◽  
Vol 11 (02) ◽  
pp. 267-282 ◽  
Author(s):  
AGAPITO LEDEZMA ◽  
RICARDO ALER ◽  
DANIEL BORRAJO
Keyword(s):  
Machine Learning ◽  
State Space ◽  
Linear Response ◽  
Search Space ◽  
Learning Systems ◽  
Hill Climbing ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Inductive Machine Learning ◽  
Simple Search

Nowadays, there is no doubt that machine learning techniques can be successfully applied to data mining tasks. Currently, the combination of several classifiers is one of the most active fields within inductive machine learning. Examples of such techniques are boosting, bagging and stacking. From these three techniques, stacking is perhaps the less used one. One of the main reasons for this relates to the difficulty to define and parameterize its components: selecting which combination of base classifiers to use, and which classifier to use as the meta-classifier. One could use for that purpose simple search methods (e.g. hill climbing), or more complex ones (e.g. genetic algorithms). But before search is attempted, it is important to know the properties of the search space itself. In this paper we study exhaustively the space of Stacking systems that can be built by using four base learning systems: C4.5, IB1, Naive Bayes, and PART. We have also used the Multiple Linear Response (MLR) as meta-classifier. The properties of this state-space obtained in this paper will be useful for designing new Stacking-based algorithms and tools.

scholarly journals Adaptive Upgradation of Personalized E-Learning Portal using Data Mining

2020 ◽  
Vol 10 (1) ◽  
pp. 224-227
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Clustering Algorithm ◽  
Learning Experience ◽  
Learning Systems ◽  
Machine Learning Techniques ◽  
Development Phase ◽  
Data Mining Techniques ◽  
E Learning ◽  
Learning Portal

Implementation of data mining techniques in elearning is a trending research area, due to the increasing popularity of e-learning systems. E-learning systems provide increased portability, convenience and better learning experience. In this research, we proposed two novel schemes for upgrading the e-learning portals based on the learner’s data for improving the quality of e-learning. The first scheme is Learner History-based E-learning Portal Up-gradation (LHEPU). In this scheme, the web log history data of the learner is acquired. Using this data, various useful attributes are extracted. Using these attributes, the data mining techniques like pattern analysis, machine learning, frequency distribution, correlation analysis, sequential mining and machine learning techniques are applied. The results of these data mining techniques are used for the improvement of e-learning portal like topic recommendations, learner grade prediction, etc. The second scheme is Learner Assessment-based E-Learning Portal Up-gradation (LAEPU). This scheme is implemented in two phases, namely, the development phase and the deployment phase. In the development phase, the learner is made to attend a short pretraining program. Followed by the program, the learner must attend an assessment test. Based on the learner’s performance in this test, the learners are clustered into different groups using clustering algorithm such as K-Means clustering or DBSCAN algorithms. The portal is designed to support each group of learners. In the deployment phase, a new learner is mapped to a particular group based on his/her performance in the pretraining program.

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.

Recommender Systems for E-Learning

2021 ◽  
pp. 221-247
Author(s):  
Mohamed Abdullah Amanullah ◽  
Abdessalem Khedher
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommendation Systems ◽  
Machine Learning Techniques ◽  
User Needs ◽  
Learning Techniques ◽  
E Learning ◽  
Minimal Deviation ◽  
Learning Platforms

The recommender systems are really important in this phase because the users want to be concentrated and to be focused on the domain in which they are interested. There should be minimal deviation in the topics suggested by the recommendation engines. Some of the famous e-learning platforms suggest recommendations based on tags such as highest rated, bestsellers, and so on in various domains. This ultimately makes the users deviate from the domain in which they have to master, and it results in not satisfying the user needs. So, to address this problem, effective recommendation engines will help provide recommendations according to the users by implementing the machine learning techniques such as collaborative filtering and content-based techniques. In this chapter, the authors discuss the recommendation systems, types of recommendation systems, and challenges.

scholarly journals A Framework for Long-Term Learning Systems

2017 ◽  
Author(s):  
Diana Benavides-Prado
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Efficient Algorithms ◽  
Learning Systems ◽  
Machine Learning Techniques ◽  
Intelligent Machines ◽  
Learning Techniques ◽  
The World ◽  
Design And Application

Increasing amounts of data have made the use of machine learning techniques much more widespread. A lot of research in machine learning has been dedicated to the design and application of effective and efficient algorithms to explain or predict facts. The development of intelligent machines that can learn over extended periods of time, and that improve their abilities as they execute more tasks, is still a pending contribution from computer science to the world. This weakness has been recognised for some decades, and an interest to solve it seems to be increasing, as demonstrated by recent leading work and broader discussions at main events in the field [Chen and Liu, 2015; Chen et al., 2016]. Our research is intended to help fill that gap.

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