scholarly journals Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1589
Author(s):  
Arijit Nandi ◽  
Fatos Xhafa ◽  
Laia Subirats ◽  
Santi Fort
Keyword(s):  
Real Time ◽  
Classification System ◽  
Data Stream ◽  
Learning System ◽  
Stochastic Gradient Descent ◽  
Emotion Classification ◽  
Learning Context ◽  
Data Set ◽  
Eeg Data ◽  
E Learning

In face-to-face and online learning, emotions and emotional intelligence have an influence and play an essential role. Learners’ emotions are crucial for e-learning system because they promote or restrain the learning. Many researchers have investigated the impacts of emotions in enhancing and maximizing e-learning outcomes. Several machine learning and deep learning approaches have also been proposed to achieve this goal. All such approaches are suitable for an offline mode, where the data for emotion classification are stored and can be accessed infinitely. However, these offline mode approaches are inappropriate for real-time emotion classification when the data are coming in a continuous stream and data can be seen to the model at once only. We also need real-time responses according to the emotional state. For this, we propose a real-time emotion classification system (RECS)-based Logistic Regression (LR) trained in an online fashion using the Stochastic Gradient Descent (SGD) algorithm. The proposed RECS is capable of classifying emotions in real-time by training the model in an online fashion using an EEG signal stream. To validate the performance of RECS, we have used the DEAP data set, which is the most widely used benchmark data set for emotion classification. The results show that the proposed approach can effectively classify emotions in real-time from the EEG data stream, which achieved a better accuracy and F1-score than other offline and online approaches. The developed real-time emotion classification system is analyzed in an e-learning context scenario.

scholarly journals Profile reliability to improve recommendation in social-learning context

2018 ◽  
Vol 44 (2) ◽  
pp. 433-454 ◽  
Author(s):  
Corinne Amel Zayani ◽  
Leila Ghorbel ◽  
Ikram Amous ◽  
Manel Mezghanni ◽  
André Péninou ◽  
...  
Keyword(s):  
Social Network ◽  
Social Learning ◽  
Learning System ◽  
Learning Context ◽  
Content Type ◽  
Semantic Relationships ◽  
The Hierarchical Structure ◽  
E Learning ◽  
Many Sources

Purpose Generally, the user requires customized information reflecting his/her current needs and interests that are stored in his/her profile. There are many sources which may provide beneficial information to enrich the user’s interests such as his/her social network for recommendation purposes. The proposed approach rests basically on predicting the reliability of the users’ profiles which may contain conflictual interests. The paper aims to discuss this issue. Design/methodology/approach This approach handles conflicts by detecting the reliability of neighbors’ profiles of a user. The authors consider that these profiles are dependent on one another as they may contain interests that are enriched from non-reliable profiles. The dependency relationship is determined between profiles, each of which contains interests that are structured based on k-means algorithm. This structure takes into consideration not only the evolutionary aspect of interests but also their semantic relationships. Findings The proposed approach was validated in a social-learning context as evaluations were conducted on learners who are members of Moodle e-learning system and Delicious social network. The quality of the created interest structure is assessed. Then, the result of the profile reliability is evaluated. The obtained results are satisfactory. These results could promote recommendation systems as the selection of interests that are considered of enrichment depends on the reliability of the profiles where they are stored. Research limitations/implications Some specific limitations are recorded. As the quality of the created interest structure would evolve in order to improve the profile reliability result. In addition, as Delicious is used as a main data source for the learner’s interest enrichment, it was necessary to obtain interests from other sources, such as e-recruitement systems. Originality/value This research is among the pioneer papers to combine the semantic as well as the hierarchical structure of interests and conflict resolution based on a profile reliability approach.

Information Security Perspective of a Learning Management System

2013 ◽  
Vol 4 (2) ◽  
pp. 9-18
Author(s):  
Mohd Faiz Hilmi ◽  
Shahrier Pawanchik ◽  
Yanti Mustapha ◽  
Hafizi Muhamad Ali
Keyword(s):  
Information Technology ◽  
Information Security ◽  
Management System ◽  
Learning Management System ◽  
Learning System ◽  
The Internet ◽  
Specific Information ◽  
Learning Context ◽  
E Learning ◽  
Learning Management

The advancement of information technology has changed the education landscape. The process of teaching is no longer the same. Information technology has made e-learning possible and available on a large scale. The main component of an e-learning is a learning management system (LMS). LMS has been widely used and are accessible through the Internet. By connected and accessed to the Internet, LMS are exposed to various threats. Proper understanding of these threats combined with strategy and best practices countermeasures will ensure a safe learning environment. Therefore this study will look into the information security aspect of LMS. There are two main purpose of this study. First, this study provides a review of information security in e-learning environments and explains the important of information security. Confidentiality, integrity and availability are considered to be the primary pillars of information security. In addition to these pillars, the International Information Systems Security Certification Consortium introduced a common body of knowledge (CBK) comprised of ten domains relating to specific information security topics. These domains are the foundation of security practices for those involved in information security. In this article, each of these is explained within an e-learning context. It is recommended that institutions employing e-learning adhere to these domains. By applying the principles and practices associated with each domain, e-learning institutions should be able to provide an e-learning system with high confidentiality, integrity and availability. The second purpose of this study is to understand student perception of an information security perspective of an e-learning management system. To achieve this purpose, a survey was conducted targeted at undergraduate students in a distance learning program. 497 students responded to a survey questionnaires. Apart from demographics information, the survey asked the respondent to rate six statements related to how they perceived security of the learning management system which they are currently using. All six statements are rated using a five point Likert scale anchored at 1 (Not at all) to 5 (Very much). Frequencies analysis was conducted to show the profile of the respondent. Overall, respondent has strong positive perceptions towards security of their LMS. This study provides an overall picture of information security elements of a learning management system. It can serve as an introduction which help LMS administrator to understand the issues and possibilities related to the safety of LMS.

Measuring E-Learners' Perceptions of Service Quality

2019 ◽  
Vol 31 (2) ◽  
pp. 83-104 ◽  
Author(s):  
Silvester Ivanaj ◽  
Grâce-Blache Nganmini ◽  
Alain Antoine
Keyword(s):  
Service Quality ◽  
Learning System ◽  
Educational Institutions ◽  
Individual Student ◽  
Learning Context ◽  
Confirmatory Factor Analyses ◽  
Five Dimensions ◽  
Determining Factors ◽  
E Learning ◽  
Attention To Students

This article examines the factors of e-learners' perceptions of service quality in terms of the physical appearance of the learning management system, students' assurance of personnel's level of knowledge, and the customized attention to students' needs. The authors use a survey to measure the five dimensions of the SERVQUAL scale, adapted to the e-learning context. A total of 325 responses were obtained. To validate their scale, the authors performed exploratory and confirmatory factor analyses. They found that the most important determining factors for e-learning are: ergonomics, corresponding to the attractiveness of the e-learning system; assurance, corresponding to instructors' ability to satisfy students' needs; and empathy, corresponding to the attention given to each individual student. The authors also found that in the context of e-learning, the relative importance of the dimensions of perceived quality is different from what is typically observed in more traditional services. Their findings enable educational institutions to improve their understanding of the expectations and perceptions of e-learners.

scholarly journals Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1798 ◽  
Author(s):  
Zeinab Shahbazi ◽  
Yung Cheol Byun
Keyword(s):  
Social Media ◽  
Computer Science ◽  
Data Science ◽  
Learning System ◽  
Local Context ◽  
Great Success ◽  
Learning Context ◽  
Wide Range ◽  
E Learning ◽  
Learning Software

Electronic Learning (e-learning) has made a great success and recently been estimated as a billion-dollar industry. The users of e-learning acquire knowledge of diversified content available in an application using innovative means. There is much e-learning software available—for example, LMS (Learning Management System) and Moodle. The functionalities of this software were reviewed and we recognized that learners have particular problems in getting relevant recommendations. For example, there might be essential discussions about a particular topic on social networks, such as Twitter, but that discussion is not linked up and recommended to the learners for getting the latest updates on technology-updated news related to their learning context. This has been set as the focus of the current project based on symmetry between user project specification. The developed project recommends relevant symmetric articles to e-learners from the social network of Twitter and the academic platform of DBLP. For recommendations, a Reinforcement learning model with optimization is employed, which utilizes the learners’ local context, learners’ profile available in the e-learning system, and the learners’ historical views. The recommendations by the system are relevant tweets, popular relevant Twitter users, and research papers from DBLP. For matching the local context, profile, and history with the tweet text, we recognized that terms in the e-learning system need to be expanded to cover a wide range of concepts. However, this diversification should not include such terms which are irrelevant. To expand terms of the local context, profile and history, the software used the dataset of Grow-bag, which builds concept graphs of large-scale Computer Science topics based on the co-occurrence scores of Computer Science terms. This application demonstrated the need and success of e-learning software that is linked with social media and sends recommendations for the content being learned by the e-Learners in the e-learning environment. However, the current application only focuses on the Computer Science domain. There is a need for generalizing such applications to other domains in the future.

scholarly journals Comparing the Effects of Stroke-Appearing and Stroke-Disappearing on Learning the Order of Strokes in Chinese Characters

2021 ◽  
Vol 12 ◽  
Author(s):  
Jon-Chao Hong ◽  
Kai-Hsin Tai ◽  
Ming-Yueh Hwang ◽  
Pei-Hsin Lin
Keyword(s):  
Learning Outcomes ◽  
Learning System ◽  
Learning Performance ◽  
Technology Enhanced Learning ◽  
Chinese Characters ◽  
School Students ◽  
Eeg Data ◽  
Significant Difference ◽  
E Learning ◽  
Writing Chinese

Different approaches to stimulating perceptions in learning can be easily designed with technology-enhanced learning systems. This study aimed to explore how different approaches can influence learners' perceptions that may negatively or positively affect their learning performance of writing Chinese characters using the correct Chinese order of strokes (COS). We therefore designed an e-learning system which was subdivided into two modes: stroke-appearing (i.e., using red to mark incorrect strokes) and stroke-disappearing (i.e., using blanks to mark incorrect strokes) to indicate strokes written in the incorrect order. We then investigated the modes that would facilitate a higher level of attention and better learning outcomes. A total of 10 third-grade elementary school students participated in the experiment, divided into two test groups. Their EEG data were collected, and time series analysis and t-tests were utilized to analyze the differences. The results indicated that: (1) there was a significant difference in the attention levels of the students practicing with the stroke-appearing and stroke-disappearing modes when learning COS, and (2) there was a significant difference in the learning outcomes of the students practicing with the stroke-appearing and stroke-disappearing modes when learning COS. These findings support the specific role of stroke order knowledge in learning Chinese characters and the need for the design of an effective method for teaching children to learn Chinese characters.

scholarly journals A Model for e-Learning Systems Success: Systems, Determinants, and Performance

2017 ◽  
Vol 12 (10) ◽  
pp. 4 ◽  
Author(s):  
Ismail M. Romi
Keyword(s):  
Information And Communication Technologies ◽  
Learning Systems ◽  
Learning System ◽  
Learning Context ◽  
Learner Performance ◽  
Learning Success ◽  
Proposed Model ◽  
System Components ◽  
E Learning ◽  
And Performance

E-learning is used by higher education institutions and corporate training institutes as a means of solving performance problems, and the accessibility to educational technology which considered as vital for acquisition and dissemination of knowledge to students, as well as interaction between instructors and students. To determine technological solutions for those institutions, an analysis to the literature, and related theories have been conducted depending on the context impact to e-learning system, as well as the interrelationship between e-learning system components and its impact on learner performance. The main findings show that e-learning system is composed of four components, mainly; the instructor, learner, course, and information and communication technologies (ICT), in addition to the context determinants of e-learning system success. The current study, proposed a model for e-learning success, which incorporates eight factors, mainly; e-learning context that include individual, institutional, and environmental determinants to e-learning success. In addition to e-learning components which include instructor, learner, course, and ICT. As well as the learner performance, that aims to measure e-learning success. The proposed model was designed to integrate prior research in the area of e-learning, where it adds set of determinants to e-learning systems success, and find out the best fit of e-learning system components. Moreover, educational institutions can use this proposed model.

scholarly journals The Latent of Student Learning Analytic with K-mean Clustering for Student Behaviour Classification

2018 ◽  
Vol 4 (2) ◽  
pp. 156
Author(s):  
Andi Besse Firdausiah Mansur ◽  
Norazah Yusof
Keyword(s):  
Student Performance ◽  
Time Management ◽  
Student Behavior ◽  
Learning System ◽  
Cluster Number ◽  
Data Set ◽  
Research Areas ◽  
Great Achievement ◽  
Future Data ◽  
E Learning

Since the booming of “big data” or “data analytic” topics, it has drawn attention toward several research areas such as: student behavior classification, video surveillance, automatic navigation and etc. This paper present k-mean clustering technique to monitor and assess the student performance and behavior as well as give improvement toward e-learning system in the future. Data set of student performance along with teacher attributes are collected then analyzed, it was filtered into 6 attributes of teacher that may potentially affect the student performance. Afterwards, k-mean clustering applied into the filtered data set to generate particular cluster number. The result reveal that Teacher1 statistically hold the highest density (0.27) and teachers with good speech/lectures tend to have strong correlation with another factor such as: commitment of teacher on preparing lecture material and time management utilization. If this synergy between teacher and student running flawlessly, it will be great achievement for e-learning system to the society.

scholarly journals Intelligent Edutab Box: Supporting Real-Time Face-to-Face Collaborative Learning

2021 ◽  
Vol 25 (2) ◽  
pp. 258-269
Author(s):  
Yuto Omae ◽  
Kazutaka Mizukoshi ◽  
Tatsuro Furuya ◽  
Takayuki Oshima ◽  
Norihisa Sakakibara ◽  
...  
Keyword(s):  
Collaborative Learning ◽  
Real Time ◽  
Information And Communications Technology ◽  
Learning System ◽  
Computer Supported Collaborative Learning ◽  
Learning Groups ◽  
Educational Benefits ◽  
Face To Face ◽  
E Learning ◽  
Learning Analysis

Educational benefits of collaborative learning have been demonstrated in several studies and various systems have been developed to date. Numerous efforts have been made to enhance these benefits by supporting collaborative learning with information and communications technology. These efforts have primarily involved support for constructing collaborative learning groups, for collaborative learning in e-learning environments, and for collaborative learning analysis. This study aims to develop a computer-supported collaborative learning system that supports instructors in real time to facilitate collaborative learning in a face-to-face environment with multiple learners at the same time to provide enhanced support. Both the learner and instructor have one tablet terminal and conduct collaborative learning in a single classroom. Herein, the learner can use the tablet to save an educational log and freely browse the educational log of another learner. By referencing the educational logs, learners can learn through face-to-face communication. Additionally, the instructor can determine (1) who is viewing whose educational log and to what extent and (2) which learner is struggling to achieve targets. Herein, an overview of the proposed system is provided and the results obtained using the proposed system are reported to evaluate its effectiveness.

Sign in / Sign up

Export Citation Format

Share Document