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Negative attitudes and perceptions on programming impair the effectiveness of learning programming skills. In this study the attitude related to programming, problem solving, and self-views on importance of IT/programming knowledge were assessed by pre- and post-test completed at the beginning and at the end of a software development course. The study was conducted using an online questionnaire and four different dimensions were measured by a survey consisting 23 items. The results show positive moderate associations between self-commitment in problem solving and algorithmic and problem solving ability and negative weak relationship with lack of self-confidence in programming. K-means algorithm showed that the students could be classified into two main groups stronger and weaker self-confidence in programming. In the case of both clusters, it was possible to achieve a positive change in attitudes related to programming. In the case of weaker self-confidence in programming, a greater change can be observed in the attitudes, which can be considered an important result from the point of view of the effectiveness of the software development course. The research presented in the article proves that attitudes related to programming can be influenced in a positive direction both in the case of those with stronger, but even more so in the case of those with weaker attitudes.

Modern technologies are now essential to make the educational process more mobile, informative and versatile. The digitization of the educational institution, taking advantage of the opportunities provided by the Internet, provides an opportunity to exchange the accumulated experience and knowledge, and provides access to information for everyone. The mandatory introduction of online education due to COVID-19 has accelerated the development of digital education. The main direction in the development of education is moving towards modern online courses, which are already being used successfully. A comprehensive concept is needed to ensure that all IT used for learning, education and training is seamless, free of barriers and closed ecosystems. In connection with the above findings this article first introduces the model and of Internet of Digital Education (IoDE), describes the principles of IoDE, and then summarize the cognitive aspects and VR relations. The purpose of this article is to help readers recognize the IoDE as a whole and predict future trends in IoDE development. [ABSTRACT FROM AUTHOR]

In today's digital era, human-computer interaction interfaces evolve and increase together with the needs of the users. However, the existing technologies have their limitations, which can hinder the efficiency of modern input devices like the Kinect sensor or other similar sensors. In this paper we improved our previous algorithm by extending it with two algorithms that aim to help telerehabilitation for individuals with movement disabilities. These two algorithms are based on the k-Nearest Neighbors, the k-Means algorithms. The algorithms are designed to accommodate the needs of the patients by adapting to their gestures based on their previous three. Using these gestures, the algorithms create multiple gesture acceptance domains around each coordinate of the gesture. Consequently, they decide whether the next user-input gesture can be considered the same movement. The accuracy of these algorithms was evaluated in three acceptance domains by comparing gesture descriptors with either the Euclidean or the Manhattan distance calculation methods. The results show that k-Nearest Neighbors algorithm yields better results in larger acceptance domains, while the k-Means algorithm can provide a better gesture acceptance rate in the smaller ones. The results show that both algorithms can be used in the telerehabilitation process, although the k-Means algorithm is more accurate than the k-Nearest Neighbors algorithm. [ABSTRACT FROM AUTHOR]

Keeping up with novel methods and keeping abreast of new applications are crucial issues in engineering education. In brain research, one of the most significant research areas in recent decades, many developments have application in both modern engineering technology and education. New measurement methods in the observation of brain activity open a new frontier in engineering applications. Electroencephalogram (EEG)-based brain activity observation processes are very promising and have been used in several engineering studies, primarily for the implementation of control tasks. This paper presents the development, implementation, and assessment of an EEG-based engineering education project, in which engineering students applied the theory they had learned and improved their knowledge and skills in the area of observation and evaluation of electrical signals generated by brain activity and measured by biosensors. The main project goal was to develop and test a brain-computer interface that is able to measure the average attention level. The effectiveness of this project-based learning was evaluated by student questionnaire responses and analysis of students' exam results; students who had participated in the project were shown to have higher levels of acquired knowledge.

Soil tillage is a very energy-intensive operation. A general expectation is to reduce energy consumption and reduce soil compaction with as few turns and interventions as possible. Thus, more and more attention is being paid to the use of active tillage machines. The aim of the present work is to test a new approach to optimize PTO-driven tilling machine operations regarding energy consumption. A real, active tillage machine, the MSS-140 type spading machine, was investigated in the Matlab® Simscape™ environment. The solid model of the spading machine was built using actual dimensions. The work done by a single spade is broken down into elementary processes. The acting forces on the implement, in each elementary process for different advancing speeds, were modelled and calculated. The model is suitable for illustrating the dynamics of loads and for calculating the mechanical work. The model was also tested in comparison with real fuel consumption. The consumed fuel quantity was measured and the energy requirement for the model calculated at three advancing speeds. A comparison between the measured and calculated energy consumption values was made: the calculated results are similar to the measured values; the mean difference is 9.91%, with a standard deviation 3.3%. [ABSTRACT FROM AUTHOR]

Abstract concepts play a vital role in decision-making or recall operations because the associations among them are essential for contextual processing. Abstract concepts are complex and difficult to represent (conceptually, formally, or computationally), leading to difficulties in their comprehension and recall. This contribution reports the computational simulation of the cued recall of abstract concepts by exploiting their learned associations. The cued recall operation is realized via a novel geometric back-propagation algorithm that emulates the recall of abstract concepts learned through regulated activation network (RAN) modeling. During recall operation, another algorithm uniquely regulates the activation of concepts (nodes) by injecting excitatory, neutral, and inhibitory signals to other concepts of the same level. A Toy-data problem is considered to illustrate the RAN modeling and recall procedure. The results display how regulation enables contextual awareness among abstract nodes during the recall process. The MNIST dataset is used to show how recall operations retrieve intuitive and non-intuitive blends of abstract nodes. We show that every recall process converges to an optimal image. With more cues, better images are recalled, and every intermediate image obtained during the recall iterations corresponds to the varying cognitive states of the recognition procedure. [ABSTRACT FROM AUTHOR]

Featured Application: Human–computer interface. In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron's weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result. [ABSTRACT FROM AUTHOR]