Online-Journals.org (International Association of Online Engineering)
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Reshaping the Museum of Zoology in Rome by Visual Storytelling and Interactive Iconography
Get PDFThis article summarizes the concept of a new immersive and interactive setting for the Zoology Museum in Rome, Italy. The concept, co-designed with all the museum’s curators, is aimed at enhancing the experiential involvement of the visitors by visual storytelling and interactive iconography. Thanks to immersive and interactive technologies designed by Centro Studi Logos, developed by Logosnet and known as e-REALâ and MirrorMeä, zoological findings and memoirs come to life and interact directly with the visitors in order to deepen their understanding, visualize stories and live experiences, and interact with the founder of the Museum (Mr. Arrigoni degli Oddi) who is now a virtualized avatar, or digital human, able to talk with the visitors. All the interactions are powered through simple hand gestures and, in a few cases, vocal inputs that transform into recognized commands from multimedia systems
Algorithms for Machine Learning with Orange System
Get PDFEmphasized is the need for new approaches and solutions for forming of increased information awareness, knowledge and competencies in the present and future generations to use the possibilities of emerging technologies for technological breakthroughs. The article presents basic machine learning tools of both types: supervised learning, which trains a model on known input and output data and predicts future results, and unsupervised learning, which finds hidden patterns or inherent structures in the input data. Algorithms for the processes of creating an information flow when applying the tools of the Orange system, which can be used for research, analysis and training, are formulated. Experiments related to smart crop production and analyses with different classification, regression and clustering algorithms. The results show that the formulated solutions can be successfully used for different tasks and can be adapted to new technologies and applications
Reducing Delay and Packets Loss in IoT-Cloud Based ECG Monitoring by Gaussian Modeling
Get PDFAbstract— Health monitoring based on the internet of things (IoT) and cloud computing is regarded as a hot topic to research. However, such systems often face issues with delay and throughput due to the large amount of data that must be transmitted from sensors to the cloud. One important type of data for health monitoring is Electrocardiogram (ECG) signals, which generate a large amount of data to be transmitted. This research treats this problem by modelling these signals in order to reduce their size using Gaussian approximation. The cloud server is an MQTT broker to which the sensors publish their data via a gateway. The Gaussian parameters are calculated in the gateway, which act as a Fog layer, before published to the broker. The monitoring devices can subscribe to the broker and access the transmitted data. Our experiments were conducted using the MIT–BIH dataset and a real broker (HiveMQ). The results showed that the system was able to significantly reduce delay in transmitting data and prevent loss of information. Without using the Gaussian approximation technique, the system was only able to monitor a limited number of patients (17 for Qos1 and 23 for Qos0) without losing information. However, when using the Gaussian approximation model with five functions, the system was able to monitor many more patients (78 for Qos1 and 100 or more for Qos0) without losing any data
Complex Thinking and Its Relevance in Professional Training: An Approach to Engineering Students in a Mexican University
Get PDFThis article aims to present the results of an analysis of the level of perceived achievement of the complex thinking competency among a group of engineering students in their last semester at a university in Western Mexico. The intention is to identify whether this population has the necessary skills to meet challenges and solve problems related to the demands of their future professional environments. Specifically, we not only seek to know the level of perception of achievement of complex thinking but also to understand its behavior and the possible existence of differences between the male and female populations. Methodologically, exploratory and multivariate descriptive statistical analyses were carried out. The results showed that the participants felt a high level of perceived achievement of the complex thinking competency, which was more noticeable in the male population
Setting Up and Implementing ArcGIS to Work with Maps and Geospatial Data with Python for Teaching Geoinformation Systems in Higher Education
Get PDFGeoinformation systems, as teaching tools in education, are closely linked to interdisciplinary and natural sciences. Currently, the application of geoinformation systems (GIS) in the educational process holds great potential for higher education. This paper presents a pedagogical system for teaching and learning a GIS course, along with a wide range of information sources that educators can choose from. The pedagogical system of introducing special GIS courses is divided into content, organizational and methodological structures. The theoretical and practical foundations of the introduction of special GIS courses are also considered. Additionally, the paper provides information about practical works setting up and implementing ArcGIS to work with maps and geospatial data with Python. The pedagogical system described in this paper was employed in the educational process of the L.N. Gumilyov Eurasian National University (Astana, Kazakhstan) and K. Zhubanov Aktobe Regional University (Aktobe, Kazakhstan). This experimental research examines how perceptions of geocapabilities evolved among 119 higher education students as they acquired technical skills in web mapping by investigating real-world issues with ArcGIS
A Bibliometric Analysis of Online Learning Emotions from 2006 to 2023
Get PDFDespite a growing body of research on online learning emotions, few studies have been committed to systematic reviews of the scientific publications in this area using bibliometric methods. Assisted by CiteSpace software, the present study aims to dissect the scientific production of this subject from 2006 to 2023 based on the database of WOS, revealing development trends and hotspots. The following findings are obtained from the bibliometric analysis. Firstly, the number of articles published has increased exponentially from only one in 2006 to 209 in 2022, demonstrating upside potential. Secondly, the United States, China and England are the most contributing countries, while Kruk, Pawlak, Kim and Artino are the most prolific authors and Cao (2020), Loderer (2020), Pekrun (2017), Li (2018), Jiang (2019), Dewaele (2018) are recognized as the most-cited articles. Lastly, studies concerning the COVID-19 pandemic and foreign language enjoyment have taken the academic high ground in the area of online learning emotions in recent years, and the research focus has shifted from negative emotions to positive ones. The findings may have implications for educators and practitioners in online learning and teaching in the future
Collaborative Innovation Ability Cultivation of College Students by Improving Achievement Transformation Efficiency
Get PDFWith the increasingly urgent demand for innovative talents, college students should have strong collaborative innovation ability because they are the backbone of future sci-tech innovation in China. Existing methods can be used to measure and evaluate to some extent the collaborative innovation and achievement transformation efficiency of college students. However, many of the methods focus on data of a certain period and neglect dynamic analysis. Dynamic comparisons of different periods need to be made because innovation activities and achievement transformation may be influenced by time factors in practical situations. In addition, some methods only focus on the quantity index of achievement transformation, and neglect quality evaluation. Therefore, this paper aimed to study the collaborative innovation ability cultivation of college students by improving the achievement transformation efficiency. Three-stage data envelopment analysis (DEA)-Windows model was used to calculate the collaborative innovation and achievement transformation efficiency of college students. This paper elaborated in detail the calculation process of these three stages, namely, the first-stage DEA-Windows analysis, the second-stage panel stochastic frontier analysis (SFA), and the third-stage adjusted DEA-Windows analysis. Based on the research needs of collaborative innovation ability cultivation of college students by improving the achievement transformation efficiency, this paper constructed an evaluation index system using Fare-Primont index model and Pastor method, and focused on the sci-tech innovation achievement transformation factors, such as output results and ability improvement. Finally, this paper showed the analysis results by combining with examples, which verified the proposed method was effective
Psychological Crisis Prediction of Students Based on Network Behavior by Big Data Mining
Get PDFSigns of psychological crisis can be found in time by analyzing the network behavior data of college students, thus providing a basis for early warning and intervention. However, existing methods may not only have shortcomings in handling dynamic data and updating models, but also rely too much on network behavior data and overlook other factors possibly affecting the psychological crisis of college students. In order to overcome these shortcomings, this paper aimed to study the psychological crisis prediction of college students based on big data mining of network behavior. Network behavior interactive prediction was defined to determine the objective function of the constructed model. Interactive prediction model framework was presented and the working principle of the model was explained. Finally, various early warning indexes, which needed to be comprehensively considered in the psychological crisis early warning model of college students, were given, and the combination of principal component analysis (PCA) and support vector machine (SVM) was applied to the construction of the early warning model, thus improving its prediction effects, generalization ability and interpretability, and reducing the overfitting risk and the difficulty of processing high-dimensional data. The experimental results verified that the constructed model was effective
Use of Big Data Technology for Network Classroom Teaching Quality Management
Get PDFQuality management of network classroom teaching has always been an urgent problem to be solved. Big data technology handles massive amounts of data and provides new quality management methods and means for network classroom teaching. However, data integration and fusion is a complex task and existing methods may not be able to deal with data fragmentation effectively, because data is often distributed across different systems and platforms in the network teaching environment. Therefore, this research aimed to study the quality management of network classroom teaching based on big data technology. This study provided a framework diagram of teaching quality evaluation criteria and factors affecting the teaching quality in the big data environment, explained complex relationships and effects among the factors, and described teaching quality prediction problems. The dimensionality reduction method of Least Absolute Shrinkage and Selection Operator (LASSO) was used for comprehensive status data integration of factors affecting teaching quality. An unequal-interval grey Riccati-Bernoulli model was constructed to study the internal relationships between various variable factors and network classroom teaching quality. Then the execution process of the prediction model, detailed modeling steps and teaching quality management steps were provided. The experimental results verified that the constructed model was effective
Use of Smart Learning Resource Management Systems for Sustainable Learning
Get PDFLearning resources are very important to all the stakeholders in the world of education. There is a very large number of learning resources today and they are scattered all over the internet. This study was about the development of a new smart learning resource management system called STOU SmartLearnX, which can locate and pull up digital content from the internet and social media. It has integrated technology to support independent learning via digital media. The system passed quality control evaluation by a panel of 15 experts before being tested on bachelor’s degree students enrolled in an online course emphasizing nanolearning style offered by an open university in Thailand. The 65 sample students testing the system came from Humanities and social science, Science and technology and Health science. They were given a pretest before using STOU SmartLearnX in the course and a posttest after using it, and their mean scores were compared using t-test dependent and one-way ANOVA. Mean posttest scores were higher than pretest scores to a statistically significant degree, but there was no significant difference in posttest scores between the students from the group, indicating that the system can be successfully used for learning in different subject areas. In the future the system may be further developed to make it able to deliver more precise results by utilizing advanced searches and images. Searches should take into account the context and should be responsive