Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The Semantic Web 10

This paper presents an evaluation of open source e-learning platforms. The main focus is on adaptation issues. The result of the evaluation shows that the platform Moodle outperforms all other platforms and also obtained the best rating in the adaptation category.

/pdf/an-evaluation-of-open-source-e-learning-platforms-stressing-p8ocmmiszr.pdf

An evaluation of open source e-learning platforms stressing adaptation issues

Background Health systems worldwide are facing shortages in health professional workforce. Several studies have demonstrated the direct correlation between the availability of health workers, coverage of health services, and population health outcomes. To address this shortage, online eLearning is increasingly being adopted in health professionals’ education. To inform policy–making, in online eLearning, we need to determine its effectiveness. Methods We performed a systematic review of the effectiveness of online eLearning through a comprehensive search of the major databases for randomised controlled trials that compared online eLearning to traditional learning or alternative learning methods. The search period was from January 2000 to August 2013. We included articles which primarily focused on students' knowledge, skills, satisfaction and attitudes toward eLearning and cost-effectiveness and adverse effects as secondary outcomes. Two reviewers independently extracted data from the included studies. Due to significant heterogeneity among the included studies, we presented our results as a narrative synthesis. Findings Fifty–nine studies, including 6750 students enrolled in medicine, dentistry, nursing, physical therapy and pharmacy studies, met the inclusion criteria. Twelve of the 50 studies testing knowledge gains found significantly higher gains in the online eLearning intervention groups compared to traditional learning, whereas 27 did not detect significant differences or found mixed results. Eleven studies did not test for differences. Six studies detected significantly higher skill gains in the online eLearning intervention groups, whilst 3 other studies testing skill gains did not detect differences between groups and 1 study showed mixed results. Twelve studies tested students' attitudes, of which 8 studies showed no differences in attitudes or preferences for online eLearning. Students' satisfaction was measured in 29 studies, 4 studies showed higher satisfaction for online eLearning and 20 studies showed no difference in satisfaction between online eLearning and traditional learning. Risk of bias was high for several of the included studies. Conclusion The current evidence base suggests that online eLearning is equivalent, possibly superior to traditional learning. These findings present a potential incentive for policy makers to cautiously encourage its adoption, while respecting the heterogeneity among the studies.

/pdf/offline-elearning-for-undergraduates-in-health-professions-a-13a3y5jxjb.pdf

Offline eLearning for undergraduates in health professions: A systematic review of the impact on knowledge, skills, attitudes and satisfaction

This literature review presents the History, Technology, and Applications of Natural Dialog Systems or simply chatbots. It aims to organize critical information that is a necessary background for further research activity in the field of chatbots. More specifically, while giving the historical evolution, from the generative idea to the present day, we point out possible weaknesses of each stage. After we present a complete categorization system, we analyze the two essential implementation technologies, namely, the pattern matching approach and machine learning. Moreover, we compose a general architectural design that gathers critical details, and we highlight crucial issues to take into account before system design. Furthermore, we present chatbots applications and industrial use cases while we point out the risks of using chatbots and suggest ways to mitigate them. Finally, we conclude by stating our view regarding the direction of technology so that chatbots will become really smart.

Chatbots: History, technology, and applications

In the last few years there has been a fast growing up of the use of Chatbots in various fields, such as Health Care, Marketing, Educational, Supporting Systems, Cultural Heritage, Entertainment and many others. This paper presents the realization of a prototype of a Chatbot in educational domain: the purpose has focused on the design of the specific architecture, model to manage communication and furnish the right answers to the student. For this aim, it has been realized a system that can detect the questions and thanks to the use of natural language processing techniques and the ontologies of domain, gives the answers to student. Finally, after the implementation of the designed model, experimental campaign was conducted in order to demonstrate its utility.

Chatbot: An Education Support System for Student

A collaborative user-centered framework for recommending items in Online Social Networks

Our "information-oriented" society shows an increasing exigency of life-long learning. In such framework, online learning is becoming an important tool to allow the flexibility and quality requested by such a kind of learning process. In the recent past, a great number of on-line platforms have been introduced on the market showing different characteristics and services. A series of features should be taken into account when one evaluates e-learning platforms, starting from the function and usability of the overall learning system in the context of the human, social and cultural organization within which it is to be used. Obviously, the analysis of the features of a system is not sufficient: it is also important to understand how they are integrated to facilitate learning and training and what principles are applied to guide the way the system is used. To evaluate them both pedagogical and technological aspects must be carefully evaluated. This paper proposes a model for describing and characterizing on-line learning platform component. The model is then used to evaluate the most known existing commercial platforms.

Evaluating on-line learning platforms: a case study

The world has changed considerably in the previous two decades. Today, people are facing extreme poverty, global warming, and unwanted climate changes. The economic gap between countries is continuously growing. Moreover, with the expanding influence of technology, governance is getting more difficult. To address these issues, the UN announced Sustainable Development Goals (SDGs), also called Global Goals, in 2015. These goals fill in as an overall source of inspiration to annihilate poverty, protect the environment, and guarantee that all individuals live in harmony and thrive by 2030. The 17 SDGs are interconnected in that they recognize that activities in a single region sway result in others and that improvement should adjust to social, monetary, and natural sustainability. The SDGs intend to kill poverty, hunger, AIDS, and gender discrimination against women and girls. The COVID-19 epidemic, on the other hand, has hampered attempts to accomplish the 2030 Agenda for Sustainable Development. As a result, the impact of these SDGs must be thoroughly studied and analyzed. As a result, the purpose of this research is to examine the SDG before and after Covid-19, as well as how they have influenced various national and international markets. The research also assesses the 17 SDGs in each of India's 29 states in depth. Since SDGs have a larger scope, this paper predicts the SDG-9 scores of few countries like UAE, New Zealand, Japan, India, Germany, China, Bhutan, and USA. 

https://doi.org/10.1016/j.stae.2022.100012

Analysis & prognosis of sustainable development goals using big data-based approach during COVID-19 pandemic

Nowadays the use of Chatbots is very popular in a large scale of applications especially in systems that provide an intelligence support to the user. In fact, to speed up the assistance, in many cases, these systems are equipped with Chatbots that can interpret the user questions and provide the right answers, in a fast and correct way. This paper presents the realization of a prototype of a Chatbot in educational domain: it has been developed a system to provide support to university students on some courses. The initial purpose has focused on the design of the specific architecture, model to manage communication and furnish the right answers to the student. For this aim, it has been realized a system that can detect the questions and thanks to the use of natural language processing techniques and the ontologies of domain, gives the answers to student. Finally, after the implementation of the designed model, experimental campaign was conducted in order to demonstrate its enforceability and efficiency.

Francesco Colace

Papers

Chatbot: An Education Support System for Student

A collaborative user-centered framework for recommending items in Online Social Networks

Evaluating on-line learning platforms: a case study

Analysis & prognosis of sustainable development goals using big data-based approach during COVID-19 pandemic

Chatbot for e-learning: A case of study