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.

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 Student Engagement Level in e-Learning Environment: Clustering Using K-means

2021 ◽  
Author(s):  
Abdallah Moubayed ◽  
Mohammadnoor Injadat ◽  
Abdallah Shami ◽  
Hanan Lutfiyya
Keyword(s):  
Learning Experience ◽  
Machine Learning Techniques ◽  
American University ◽  
Performance Metric ◽  
Learning Techniques ◽  
Event Log ◽  
E Learning ◽  
Level Model ◽  
Learning Platforms ◽  
Undergraduate Science

E-learning platforms and processes face several challenges, among which is the idea of personalizing the e-learning experience and to keep students motivated and engaged. This work is part of a larger study that aims to tackle these two challenges using a variety of machine learning techniques. To that end, this paper proposes the use of k-means algorithm to cluster students based on 12 engagement metrics divided into two categories: interaction-related and effort-related. Quantitative analysis is performed to identify the students that are not engaged who may need help. Three different clustering models are considered: two-level, three-level, and five-level. The considered dataset is the students’ event log of a second-year undergraduate Science course from a North American university that was given in a blended format. The event log is transformed using MATLAB to generate a new dataset representing the considered metrics. Experimental results’ analysis shows that among the considered interaction-related and effort-related metrics, the number of logins and the average duration to submit assignments are the most representative of the students’ engagement level. Furthermore, using the silhouette coefficient as a performance metric, it is shown that the two-level model offers the best performance in terms of cluster separation. However, the three-level model has a similar performance while better identifying students with low engagement levels.

scholarly journals Survey of Machine Learning Techniques for Student Profile Modeling

2021 ◽  
Vol 16 (04) ◽  
pp. 136
Author(s):  
Touria Hamim ◽  
Faouzia Benabbou ◽  
Nawal Sael
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Machine Learning Techniques ◽  
Student Profile ◽  
Learning Techniques ◽  
Survey Results ◽  
Learning Platforms ◽  
Recruitment System ◽  
State Of Art ◽  
Profile Modeling

Developments in information technology have led to the emergence of several online platforms for educational purposes, such as e-learning platforms, e-recommendation systems, e-recruitment system, etc. These systems exploit advances in Machine Learning to provide services tailored to the needs and profile of students. In this paper, we propose a state of art on student profile modeling using machine learning techniques during last four years. We aim to analyze the most used and most efficient machine learning techniques in both online and face-to-face education context, for different objectives such as failure, dropout, orientation, academic performance, etc. and also analyze the dominant features used for each objective in order to achieve a global view of the student profile model. Decision Tree is the most used and the most efficient by most of research studies. And academic, personal identity and online behavior are the top characteristics used for the student profile. To strengthen the survey results, an experiment was carried out, based on the application of machine learning techniques extracted from the state of art analysis, on the same datasets. Decision tree gave the highest performance, which confirms the survey results.

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.

Stress Detection Using Machine Learning Classifiers in Internet of Things Environment

2019 ◽  
Vol 16 (10) ◽  
pp. 4214-4219
Author(s):  
Richa Sharma ◽  
Shalli Rani ◽  
Deepali Gupta
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Recommender Systems ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Healthcare ◽  
Learning Techniques ◽  
Internet Of Things Environment ◽  
Novel Applications

Over the years, Recommender systems have emerged as a means to provide relevant content to the users, be it in the field of entertainment, social-network, health, education, travel, food or tourism. Further,with the expeditious development of Big Data and Internet of Things (IoT), technology has successfully associated with our everyday life activities with smart healthcare being one. The global acceptance towards smart watches, wearable devices or wearable biosensors have paved the way for the evolution of novel applications for personalized eHealth and mHealth technologies. The data gathered by wearables can further be interpreted using Machine learning algorithms and shared with healthcare experts to provide suitable recommendations. In this work, we study the role of recommender systems in IoT and Cloud and vice-versa. Further, we have analyzed the performance of different machine learning techniques on SWELL dataset. Based on the results, it is observed that 2 Class Neural network performs the best with 98% accuracy.

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