Подобряване ефективността на учене чрез участие в онлайн семинар: Aнализ чрез машинно обучение

Learning performance is related to students’ learning activities during a learning process. Their learning behavior could lead to successful course accomplishment or not, to better or worse final marks. Seminar practices have their impact on development of some students’ competences like: topics analysis, discussion and presentation and the planned tasks concern learning performance. In online environment, the seminars could be organized in the form of different learning scenarios and it depends on the functional and technical features of the organized educational environment as well as on the course goal. In this paper an investigation and analysis of students’ participation in online seminars is conducted with aim to understand the dependence between their learning performance, online tasks realization and final results. eLearning informatics gives possibilities for usage contemporary methods for research and learning analytics as one of them is machine learning. Machine learning algorithms are utilized to group students according to their learning behavior and final outcome. The created analytical models could be in support of educators and students to improve their educational activities. The accuracy of machine learning algorithms is evaluated to find the best model according to collected data during one semester.Изследването е подпомогнато от Фонд „Научни изследвания“ по проект „Моделиране и изследване на интелигентни системи за обучние и сензорни мрежи (ИСОСеМ)“, договор КП-06-Н47/4 от 26.11.2020

Understanding microblogging hashtags for learning enhancement

The use of hashtags in Twitter messages is growing among the educational community for various reasons. Exploring and understanding how hashtags can facilitate learning and self-learning organization is the main aim of this paper. Several online available tools for hashtags management are examined and their recognition and utilization can assist knowledge gathering and learning network constructing

Professional And Personal Competence Of Healthcare Specialists And Inclination For Training

The management of the professional and personal competence of health professionals is one of the main tasks of modern management. A training strategy has been developed, based on empirical assessment of the needs of the target group of healthcare professionals in the Clinic of Pediatric Diseases, Dr. Georgi Stranski University Hospital, Pleven. The aim of the study is to outline the overall picture of professional and personal competence among working nurses to point out possible and necessary areas of professional competence that can be improved through training and to build a training program on this basis. The statistical processing of the data from the study was done with SPSS 16.0 and Excel, performing demographical statistics, percent ratio, average values of the individual factors, and correlation analysis. In the developed training program, in the first place there are topics related to learning communication skills, ability to work with aggressive parents, and skills to talk with parents. Pedagogical skills and competences, and abilities to use information technologies are third among all planned topics, followed by courses on coping strategies for dealing with stress and decision-making skills

Техники за запазване на поверителността и техни приложения в електронното обучение

The paper summarizes contemporary methods and techniques for privacy preservation as some challenging issues are analyzed and presented. A bibliometric approach is utilized in order for the "big picture" to be outlined, showing current research status and trending topics. The bibliographic data are taken from scientific database Scopus and processed through specialized software. In addition, a detailed review is also performed to classify problems and solutions in the area of privacy preservation. Special attention is given to possibilities for data privacy protection in intelligent eLearning environments. The role of machine learning for creating more secure data models is pointed out. A conceptual model, summarizing the findings, is proposed.This research is supported by the Bulgarian FNI fund through the project “Modeling and Research of Intelligent Educational Systems and Sensor Networks (ISOSeM)”, contract КП-06-Н47/4 from 26.11.2020

Development of AI-Based Tools for Power Generation Prediction

This study presents a model for predicting photovoltaic power generation based on meteorological, temporal and geographical variables, without using irradiance values, which have traditionally posed challenges and difficulties for accurate predictions. Validation methods and evaluation metrics are used to analyse four different approaches that vary in the distribution of the training and test database, and whether or not location-independent modelling is performed. The coefficient of determination,R2, is used to measure the proportion of variation in photovoltaic power generation that can be explained by the model’s variables, while gCO2eq represents the amount of CO2 emissions equivalent to each unit of power generation. Both are used to compare model performance and environmental impact. The results show significant differences between the locations, with substantial improvements in some cases, while in others improvements are limited. The importance of customising the predictive model for each specific location is emphasised. Furthermore, it is concluded that environmental impact studies in model production are an additional step towards the creation of more sustainable and efficient models. Likewise, this research considers both the accuracy of solar energy predictions and the environmental impact of the computational resources used in the process, thereby promoting the responsible and sustainable progress of data science.This research is supported by the Bulgarian National Science Fund in the scope of the project ”Exploration the application of statistics and machine learning in electronics” under contract number κπ-06-H42/1

L’uso dell’hashtag nel microblogging per migliorare l’apprendimento

L'uso dell’hashtag nei messaggi di Twitter sta crescendo tra la comunità educativa per vari motivi. Esplorare e capire come l’hashtag possa facilitare l’organizzazione dell'apprendimento e dell’auto-apprendimento è l'obiettivo principale di questo contributo. Vengono esaminati diversi strumenti disponibili online per la gestione dell’hashtag: la loro analisi ed utilizzazione può supportare l’assemblaggio di conoscenze e la costruzione di una rete di apprendimento