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Artificial intelligence (AI) and deep learning (DL) techniques are increasingly used in education because of advancements in online learning platforms and their ongoing implementation. The existing methods suffer from low-processing efficiency, high prediction error, and increased memory requirements when faced with vast learning and student behavior data. Thus, based on DL, this research suggests a way to analyze student behavior in e-learning. Data on student behavior are gathered, and a learning behavior model for online learning is created. The proposed optimal DL approach aims to screen the collected behavior data using data preparation, analysis, and statistics. Additionally, the Pearson correlation coefficient (PCC) approach is employed to determine the degree of data similarity. The novelty of the research is followed by utilizing an optimized DL network, known as a deep neural network with red deer optimization (ODNN-RDO), to mine students’ behavior data in e-learning programs. Two datasets, metrics including accuracy, precision, and recall, together with error measures like relative error, the root mean square error (RMSE), and absolute error, are utilized to test the created models. The improved generated models achieved 98.15% accuracy and 0–0.04% error compared to the current methods. The optimization procedure subsequently optimizes the components to acquire the best outcomes regarding faculty and parent performance monitoring of students. With effective monitoring, this model maximizes the e-learning platform for planning student growth.

Researchers have shown the limitations of using the single-modal data stream for emotion classification. Multi-modal data streams are therefore deemed necessary to improve the accuracy and performance of online emotion classifiers. An online decision ensemble is a widely used approach to classify emotions in real-time using multi-modal data streams. There is a plethora of online ensemble approaches; these approaches use a fixed parameter ($\beta$) to adjust the weights of each classifier (called penalty) in case of wrong classification and no reward for a good performing classifier. Also, the performance of the ensemble depends on the $\beta$, which is set using trial and error. This paper presents a new Reward-Penalty-based Weighted Ensemble (RPWE) for real-time multi-modal emotion classification using multi-modal physiological data streams. The proposed RPWE is thoroughly tested using two prevalent benchmark data sets, DEAP and AMIGOS. The first experiment confirms the impact of the base stream classifier with RPWE for emotion classification in real-time. The RPWE is compared with different popular and widely used online ensemble approaches using multi-modal data streams in the second experiment. The average balanced accuracy, F1-score results showed the usefulness and robustness of RPWE in emotion classification in real-time from the multi-modal data stream.

The Chronobot/Virtual Classroom (CVC) system is a novel time knowledge exchange platform where any pair of users can exchange their time and knowledge. User profile that contains user attributes, preferences, and learning patterns serves as a primary basis to identify exchange partners and determine exchange rates. In this paper, we described the methodology to acquire knowledge about users i.e. user profile from their activities. The association between user profile and user behaviors (e.g. online reading, chatting and time/knowledge exchanging) is identified by several feedback indicators extracted from browsing history, chatting session and exchange transaction. A linear learning model is constructed to fuse multiple feedback indicators to infer user preference. The methods utilizing user profile to identify the exchange partners and determine the exchange rate are also described in detail.

We tackle the problem of knowledge mining on the Web. In this paper, we propose MGKM algebraic system for iterative search documents sets, and then develop an approach to extract topics on the web with Multi-Granularity Knowledge Mining algorithm (MGKM). The proposed approach maps the data space of the original method to a vector space of sentence, improving the original DBCO algorithm. We outline the interface between our scheme and the current data Web, and show that, in contrast to the existing approaches, no exponential blowup is produced by the MGKM. Based on the experiments with real-world data sets of 310 users in three study sites, we demonstrate that knowledge mining in the proposed approach is efficient, especially for large-scale web learning resources. According to the user ratings data of four learning sites in the 150 days, the average rate of increase of user rating after the system is used reaches 25.18%.

Engaging students’ personalized data in the aspects of education has been on focus by different researchers. This paper considers it vital for exploring the student’s progress, moreover, it could predict the student’s level which consequently leads to identifying the required student material to raise his current education level. Although the topic has been vital before the COVID-19 pandemic, however, the importance of the topic has increased exponentially ever since. The research supports the decision-makers in educational institutions as considering personalized data for the student’s educational tasks and activities proved the positive impact of raising the student level. The paper proposes a framework that considers the students’ personal data in predicting their learning skills as well as their educational level. The research included engaging five well-known clustering algorithms, one of the most successful classification algorithms, and a set of 10 features selection techniques. The research applied two main experiment phases, the first phase focused on predicting the students’ learning skills, and the second focused on predicting the students’ level. Two datasets are involved in the experiments and their sources are mentioned. The research revealed the success of the clustering and prediction tasks by applying the selected techniques to the datasets. The research concluded that the highest clustering algorithm accuracy is enhanced k-means (EKM) and the highest contributing features selection method is the evolutionary computation method.

Collaborative learning environments require intensive, regular and frequent analysis of the increasing amount of interaction data generated by students to assess that collaborative learning takes place. To support timely assessments that may benefit students and teachers the method of analysis must provide meaningful evaluations while the interactions take place. This research proposes machine learning-based techniques to infer the relationship between student collaboration and some quantitative domain-independent statistical indicators derived from large-scale evaluation analysis of student interactions. This paper (i) compares a set of metrics to identify the most suitable to assess student collaboration, (ii) reports on student evaluations of the metacognitive tools that display collaboration assessments from a new collaborative learning experience and (iii) extends previous findings to clarify modeling and usage issues. The advantages of the approach are: (1) it is based on domain-independent and generally observable features, (2) it provides regular and frequent data mining analysis with minimal teacher or student intervention, thereby supporting metacognition for the learners and corrective actions for the teachers, and (3) it can be easily transferred to other e-learning environments and include transferability features that are intended to facilitate its usage in other collaborative and social learning tools.

The expansion of the population that wants to learn online is growing due to several e-learning platforms, which help innovate and suggest courses to learners. Several techniques are devised for determining optimal courses for the learner. In recent days, researchers began to utilize recommendation systems in e-learning. This paper devises a novel technique for course recommendation to students in an e-learning platform, which helps learners select the best course. Here, the Butterfly Weed Optimization (BWO) is newly devised by combining Invasive Weed Optimization (IWO) and Butterfly Optimization Algorithm (BOA). At first, the process is performed by inputting the data to the Course subscription matrix for constructing the matrix based on learner interest and courses. Here, course grouping is performed using Interval type-2 Fuzzy Local Enhancement Based Rough K-means Clustering. Furthermore, the course is matched with input data based on entropy and angular distance. Finally, the sentiment classification is performed using the Ontology-based approach SentiWordNet and Deep Neural Network (DNN). Here, the DNN is trained with the proposed BWO algorithm, and thus the course recommendation is attained by offering a suitable course recommendation to learners.

Colleges and universities increasingly incorporate ideological and political (IP) concepts into their courses as a fundamental prerequisite and a rising IP education trend under changing conditions. Students have difficulty sifting through the ever-growing amount of online information to locate what they need in learning resources. Technology-enhanced learning encompasses any technology that helps students study more effectively. This paper suggests a personalized learning resource recommendation system (PLRRS) for IPC. Personal learning recommendation systems (PLRSs) that do their task well will help students cope with the existing information overload. They will make sure that they receive the correct information at the right time and in the right format for their particular needs. E-learning systems that intentionally personalize their courses to the preferences, objectives, skills, and interests of the students they serve are engaging in personalized learning. In the last several years, researchers have been looking at ways to assist instructors in enhancing e-learning. Personalized learning scenarios are created by picking the most relevant learning objects based on an individual’s profile. A test score greatly improved for students in IPC after using the model in this research, which suggests that this model has a strong promotion value.

Employee training is essential for corporate activities to improve their production efficiency and product quality. The most representative two types of employee training are known, i.e., on-the-job training and off-the-job training. Off-the-job training can be classified into two types, i.e., compulsory training and non-compulsory training. Safety training and compliance are known as compulsory training, and they are needed to ensure that daily work continues without serious problems. Compulsory education is undertaken in a classroom every year by all employees of a department or section. Daily e-learning is effective for complementing and enhancing mandatory education and it helps employees to remember what they have learned during their annual education. In this paper, we discuss optimal employee safety education models that are complemented and enhanced by e-learning. The expected cost rate of education is expressed using the imperfect maintenance model, and the optimal policies that minimize it are discussed.

Transliteration is the process of mapping the character of one language to the character of some other language based on its phonetics. India is very much diverse in languages where people speak different languages. Though they speak different languages, it might be difficult for them to read the script of those many languages. In a situation like this, transliteration process plays a major role. It helps in various Natural Language Processing applications such as Information retrieval, Machine translation, Speech recognition. These are NLP applications which make the computer understand the natural language as to how human being interprets. It helps in translating technical terms and proper names from one language to another language. Moreover, transliteration works have been carried out in languages such as Japanese, Chinese and English. But when considering Indian languages, especially Tamil language, very few recognizable works have been carried out. In this paper, transliteration process is carried out on Unicode Tamil characters. The phonetics-based forward list processing is implemented for transliterating from English language to Tamil language which yields promising results.

Mundipharma and Helsinn Group Expand Exclusive Licensing and Distribution Agreements for Leading Anti-emetic Products in Middle East, Africa, Latin America and Indonesia.

Leading Regional Medical Technology Trade Associations Reinforce their Commitment to Evidence-based Healthcare.

Lonza Expands Airway Disease Portfolio with Addition of IPF Airway Cells.

Boston Scientific Launches Interventional Cardiology Online Education Portal for Physicians.

Pfizer Presented Data from PALOMA-2 Phase 3 Study Demonstrating Clinical Benefit of IBRANCE^® (palbociclib) in Asian Women with ER+, HER2- Metastatic Breast Cancer.

Global Healthcare Systems at Pivotal Point as Technology Offers Solutions to Industry Challenges – The Economist Intelligence Unit.

Cellectricon and Censo Biotechnologies Introduce a Joint Technology Access Program Utilizing High-Quality Human iPSC-based Discovery Services for CNS and Pain Research.

As the home computer and the Internet are becoming more and more popular, social service agencies in Hong Kong are beginning to show interests in making use of the new technology to extend social welfare services to the community. This paper presents the results of an empirical study to evaluate the Cyber-Parenting Project as a pioneer attempt in providing parenting education through the Internet and gives recommendations for future attempts of similar nature. The discussion covers the conceptualisation, design, implementation and utility of the Cyber-Parenting Project, and the recommendations include issues on system design, provision, testing, and monitoring of web-based social service programs.

由於家用電腦及萬維網的應用日益普及，本港的社會服務機構亦開始思考如何應用這新科技去進一步延展社會福利服務到社區；而“Cyber親職教育網”便是利用萬維網去提供親職教育的一項創新計劃。本文就對該計劃進行的實證研究結果去評估該計劃在構思、設計、執行、及效用各方面的得失，並因應評估結果作出關乎系統設計、提供、測試、及監控等多方面的建議，供有興趣發展網上服務的福利機構及人仕參考。

SCORM (Sharable Content Object Reference Model) is one of the international e-learning specifications, which provides sharable content, compatible run-time environment and learning profile. SCORM supports accessibility, adaptability, affordability, durability, interoperability and reusability. People can share their own learning content with each other and learn things on the Internet. SCORM learning content can run on the SCORM learning management system. However SCORM do not have a complete evaluation mechanism. The SP chart represents students and problems. It is a tool to analyze the relationship between students and their answers to test problems. In this paper, we integrate the SP chart into SCORM as a formative assessment and add course as the third dimension to strengthen SCORM assessment. The developed tool can be used for SCORM assessment in three perspectives: student-problem, course-problem, and student-problem. So any test problem set and the student performance can be thoroughly examined. The tool was applied to a class and the empirical results are presented in this paper.

Artificial Intelligence (AI) assisted educational institutions extensively utilize electronic learning context to guarantee improved teaching and learning experiences accompanied by educational activities. E-learning or online learning plays a significant role in Chinese higher education. There is a challenge to implement e-learning in China’s higher education to improve course resources, student learning style prediction, teaching quality, and service support. Hence in this paper, Artificial Intelligence based Efficient E-learning Framework (AI-EELF) has been proposed to overcome the challenges faced by China’s higher education while implementing e-learning modules. The collected student data can be efficiently utilized and exploited to progress in an adaptive learning environment. The proposed AI-EELF method introduces multiple learning models to enhance teaching quality and predict the student learning style. The experimental results show that the proposed AI-EELF achieves high performance, prediction ratio in determining students’ learning style and improves teaching quality compared to other existing methods.

As Internet computing becomes a norm in everyday life, web-based learning is acceptable to many people. However, it is a challenge to learn data modeling on the Internet. Not only is it difficult to guide the users through the operations of e-Learning, but also it is hard to measure its effectiveness. This paper presents a methodology of using interactivity of guiding the user through a case study to learn the discipline of stepwise procedure of data modeling. With well documented data modeling theories on the web pages, the user has to answer the question correctly by selecting the right choice step by step to complete a case study. As a result, the user learns the correct way of data modeling through interactive learning on the web. It is very user friendly and effective. In this respect, this paper focuses on the design principles and implementation of the Human-Computer Interface (HCI). We lay down the guidelines for HCI design concept and principles, and evaluate the HCI design of an interactive e-Learning application.

The virtual programming lab (VPL) project described in this paper is designed to facilitate Internet access to application software. It emulates a real computing laboratory environment that promotes group learning and project management. The laboratory resources are situated at the university and are centrally controlled. Users of the virtual programming laboratory include students, tutors and administrators of the system. Users can be located at different geographical locations to remotely access applications through the Internet. The virtual programming lab design is based on a distance education concept.

This paper focuses on the design and development of the runtime modules within the VPL framework. These runtime modules provide underlying services that drive the launching of applications, file management and communications services. In addition, this paper presents an evaluation of the performance of the system.

The Internet has promoted several changes in the world. In this scenario, new theories, paradigms and methodologies have been discussed in the education area. In addition, as the development of learning material is expensive and time-consuming, the use of Learning Objects (LO) has been proposed for improving content reuse. However, the emphasis has been on how to describe LO, but it is not clear how to develop them. In addition, people want to satisfy their learning needs according to their personal characteristics such as knowledge background and learning style. The work presented in this paper proposes the increase of LO semantics. The idea behind our proposal is to develop LO based on a model for structuring knowledge (concepts, demonstrations and interactions) according to their context, representation and composition. This approach enables increased reusability and adaptability while providing better structured content material.

This paper re-visits the ongoing e-learning phenomenon from a holistic perspective through the study of the world's first nationwide, e-inclusive society in Singapore. It draws upon the current literature for learning models, knowledge management and utilization of technology for electronic education to examine the contribution Singapore's National IT Literacy Program (NITLP) has made to drive the diffusion of Internet, broadband and e-transaction adoption in this island nation. In doing so, the case study tracks NITLP's development of continuous and progressive phases of infrastructure, infostructure and knowledge-structure integration to leverage Information and Communication Technologies (ICTs) more efficiently and effectively for the delivery of a holistic learning experience for Singapore's late IT adopters. The practical application of the implementation program is the provision of an evaluative tool for e-learning practitioners to enable an upward spiral of continuous feedback and improvements to the e-learning architecture and contribute more strategically to the development of a total learning experience. For the learner, the model generates strategic value from knowledge creating and sharing activities. The lessons learnt from Singapore's approach to developing its e-inclusive society are significant beyond just the experience of this island nation, as it serves as an indicator of how countries in the Asia-Pacific and internationally can conceptualize and implement national IT literacy programs within a framework of e-inclusive societies to promote knowledge-based economies.

This paper investigates the sorts of risks and uncertainties inherent in implementing an e-learning information systems project in Estonia. The study uses a variation of the Delphi study in eliciting the risk factors or items from experienced top management professionals within the organisation. The main objective of the study is to identify the uncertainties or risks in the implementation of the systems, using the viewpoint of Estonia, which is an emerging economy. The findings of the work indicate that wrong development strategy, staff volatility, change in top management and lack of funding are amongst the top risk factors in implementing e-learning in Estonia. On the other hand, risks emanating from users' involvement and commitment seem to be viewed as less critical to the success of the project.

This paper explores user views of the current e-learning practices in an organisational setting. Eighty employees of Korean Air, who were enrolled in an e-learning course, participated in the study on a voluntary basis. Data on employees' perceptions of importance and satisfaction with their course portal were gathered by administering a survey questionnaire. The study revealed that employees considered all portal features as quite important and satisfying, although some more than others. They also agreed that there was scope for further improvement through adding extra functionality. These findings have important implications for improving the effectiveness of corporate e-learning.