scholarly journals Provisioning computational resources for cloud-based e-learning platforms using deep learning techniques

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jorge Ariza ◽  
Miguel Jimeno ◽  
Ricardo Villanueva-Polanco ◽  
Jose Capacho
Keyword(s):  
Deep Learning ◽  
Learning Techniques ◽  
E Learning ◽  
Learning Platforms ◽  
Computational Resources

An overview of deep learning techniques

2018 ◽  
Vol 66 (9) ◽  
pp. 690-703 ◽  
Author(s):  
Michael Vogt
Keyword(s):  
Deep Learning ◽  
Single Layer ◽  
Visual Image ◽  
Image Understanding ◽  
Number Of Layers ◽  
Box Models ◽  
Learning Techniques ◽  
Engineering Work ◽  
Computational Resources ◽  
Black Box Models

Abstract Deep learning is the paradigm that profoundly changed the artificial intelligence landscape within only a few years. Although accompanied by a variety of algorithmic achievements, this technology is disruptive mainly from the application perspective: It considerably pushes the border of tasks that can be automated, changes the way products are developed, and is available to virtually everyone. Subject of deep learning are artificial neural networks with a large number of layers. Compared to earlier approaches with ideally a single layer, this allows using massive computational resources to train black-box models directly on raw data with a minimum of engineering work. Most successful applications are found in visual image understanding, but also in audio and text modeling.

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 Applying E-learning Technologies to Library Information Literacy Instruction

2010 ◽  
pp. 227-243
Author(s):  
Jamie Ward
Keyword(s):  
Information Literacy ◽  
Teaching Styles ◽  
Learning Technologies ◽  
Academic Librarians ◽  
Instructional Content ◽  
Learning Techniques ◽  
E Learning ◽  
Learning Platforms ◽  
Instructional Interaction

Academic libraries have adopted and adapted the e-learning technologies for delivery of their Information Literacy programmes. This chapter describes some of the ways in which academic librarians have been very inventive in using emerging technologies to enhance their instructional content. By using a case study of DkIT the chapter details how information literacy and the e-learning technologies emerged together. E-learning platforms like the virtual learning environments (VLE) are the natural place for libraries to use as portals for their IL instruction. This chapter argues that using the VLE (with the inherent instructional interaction made possible by this technology), and adopting some amalgam of the newer teaching styles like problem-based learning and blended learning techniques completes the IL circle for librarians. Librarians now have the tools at their disposal to finally fulfil the promises we undertook when we embarked on our information literacy programmes.

scholarly journals Applying E-learning Technologies to Library Information Literacy Instruction

2012 ◽  
pp. 693-709
Author(s):  
Jamie Ward
Keyword(s):  
Information Literacy ◽  
Teaching Styles ◽  
Learning Technologies ◽  
Academic Librarians ◽  
Instructional Content ◽  
Learning Techniques ◽  
E Learning ◽  
Learning Platforms ◽  
Instructional Interaction

Academic libraries have adopted and adapted the e-learning technologies for delivery of their Information Literacy programmes. This chapter describes some of the ways in which academic librarians have been very inventive in using emerging technologies to enhance their instructional content. By using a case study of DkIT the chapter details how information literacy and the e-learning technologies emerged together. E-learning platforms like the virtual learning environments (VLE) are the natural place for libraries to use as portals for their IL instruction. This chapter argues that using the VLE (with the inherent instructional interaction made possible by this technology), and adopting some amalgam of the newer teaching styles like problem-based learning and blended learning techniques completes the IL circle for librarians. Librarians now have the tools at their disposal to finally fulfil the promises we undertook when we embarked on our information literacy programmes.

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.

Evaluation of Students’ Flow State in an E-learning Environment Through Activity and Performance Using Deep Learning Techniques

2020 ◽  
pp. 073563312097983
Author(s):  
Yusuf Can Semerci ◽  
Dionysis Goularas
Keyword(s):  
Learning Environment ◽  
Deep Neural Networks ◽  
Complex Task ◽  
Flow State ◽  
Learning Platform ◽  
Online Lectures ◽  
Learning Techniques ◽  
E Learning ◽  
Learning Platforms ◽  
And Performance

Estimating the flow state of students in a course allows evaluating their sentimental state and the challenges they are facing. In e-learning platforms, the evaluation of flow state is a complex task because it depends on the ability to extract the parameters that better reflect the activity and effort of students. In this scope, the current study proposes a method based on flow theory aiming to provide information about the students' flow state in a course that is taught in an e-learning environment. First, the interaction of students with an e-learning platform that comprises classical e-learning pages and a timeline tool is analyzed, using activity heatmaps and deep neural networks. Then, by taking also in account their grades, the flow state of students is calculated. The resulted data are validated with a statistical analysis that also utilizes student surveys. In order to guarantee that this method is applicable to various profiles, students from different faculties participated in this study. In a period where education is rapidly adapting to online lectures and e-learning platforms, the estimation of student's flow state in e-learning environments can provide useful feedback and data to students and educators.

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