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.

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 Design and Implementation of a Gesture-Aided E-Learning Platform

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8042
Author(s):  
Wolfgang Kremser ◽  
Stefan Kranzinger ◽  
Severin Bernhart
Keyword(s):  
Learning Outcomes ◽  
Field Test ◽  
Learning Experience ◽  
Learning Content ◽  
Learning Platform ◽  
System Usability Scale ◽  
E Learning ◽  
Learning Platforms ◽  
Reference Implementation ◽  
Generic System

In gesture-aided learning (GAL), learners perform specific body gestures while rehearsing the associated learning content. Although this form of embodiment has been shown to benefit learning outcomes, it has not yet been incorporated into e-learning. This work presents a generic system design for an online GAL platform. It is comprised of five modules for planning, administering, and monitoring remote GAL lessons. To validate the proposed design, a reference implementation for word learning was demonstrated in a field test. 19 participants independently took a predefined online GAL lesson and rated their experience on the System Usability Scale and a supplemental questionnaire. To monitor the correct gesture execution, the reference implementation recorded the participants’ webcam feeds and uploaded them to the instructor for review. The results from the field test show that the reference implementation is capable of delivering an e-learning experience with GAL elements. Designers of e-learning platforms may use the proposed design to include GAL in their applications. Beyond its original purpose in education, the platform is also useful to collect and annotate gesture data.

Optimization Scenarios for Open Source Software Used in E-Learning Activities

2018 ◽  
pp. 102-123
Author(s):  
Utku Köse
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Learning Experience ◽  
Learning Activities ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Source Codes ◽  
E Learning ◽  
Additional Costs ◽  
Teaching Learning

Using open software in e-learning application is one of the most popular ways of improving effectiveness of e-learning-based processes without thinking about additional costs and even focusing on modifying the software according to needs. Because of that, it is important to have an idea about what is needed while using an e-learning-oriented open software system and how to deal with its source codes. At this point, it is a good option to add some additional features and functions to make the open source software more intelligent and practical to make both teaching-learning experiences during e-learning processes. In this context, the objective of this chapter is to discuss some possible applications of artificial intelligence to include optimization processes within open source software systems used in e-learning activities. In detail, the chapter focuses more on using swarm intelligence and machine learning techniques for this aim and expresses some theoretical views for improving the effectiveness of such software for a better e-learning experience.

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 Promoting Students’ Global Perspectives Through a Gamified e-Learning Platform

2021 ◽  
Vol 6 ◽  
Author(s):  
Frankie Y. W. Leung ◽  
Martin Lau ◽  
Kelvin Wan ◽  
Lisa Law ◽  
Theresa Kwong ◽  
...  
Keyword(s):  
Learning Experience ◽  
Global Perspectives ◽  
Global Issues ◽  
Learning Platform ◽  
Before And After ◽  
E Learning ◽  
Learning Platforms ◽  
Cognitive Knowledge ◽  
Survey Responses ◽  
Development Of Students

With the rapid growth of internationalization in tertiary institutions worldwide, the development of students’ global perspectives has attracted the attention of many universities. However, this development is a challenging one due to the complicated nature of global issues and their incompatibility with traditional subject-specific boundaries of classroom teaching. Through two eTournaments organized on a proprietary gamified e-learning platform named “PaGamO,” this study examined participating students’ learning experience and their change of global perspectives due to their participation in the eTournaments. Data were collected before and after the two eTournaments, and 217 survey responses were considered to be valid and were further analyzed. The findings showed that participating students achieved the satisfaction level of enjoyment (M = 3.62) and their awareness of the United Nations Sustainable Development Goals (SDGs) (M = 3.96) had been improved. In addition, the findings also revealed that 1) students enjoyed and perceived a better understanding of the SDGs in terms of perceptual dimensions like value-oriented and partnership-oriented, rather than the global issues about substantial threats or environmental issues; 2) the “intrapersonal effect” of students had been significantly reduced after the eTournaments; 3) positive significant correlations were found between the level of enjoyment and frequency of question-attempt in relation to the change of cognitive knowledge and interpersonal social interaction. The findings of this study offered some possible insights into students’ gameplay experience concerning dimensions of global perspectives and also support the findings of prior research on how gamified e-learning platforms could contribute to the development of students’ global perspectives.

A Novel Predictability Performance Metric and Its Forecast Using Machine Learning Techniques

2018 ◽  
Author(s):  
Rocio Barragan Montes ◽  
Ana Delcan Rojas ◽  
Victor Fernando Gomez Comendador ◽  
Rosa Arnaldo Valdes ◽  
Luis Perez Sanz
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Performance Metric ◽  
Learning Techniques

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

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.

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.

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