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Computer-Supported Collaborative Learning is a promising innovation that ameliorates tutoring through modern technologies. However, the way of recommending collaborative activities to learners, by taking into account their learning needs and preferences, is an important issue of increasing interest. In this context, this paper presents a framework for providing recommendations of collaborative activities to learners by employing an Artificial Neural Network (ANN) and the Weighted Sum Model (WSM). In our approach, the Gardner and Korth framework is employed to identify the dimensions of the learners' collaborative learning styles. For each dimension, different weights are assigned and used as input data for the ANN. Then, the WSM and the activation function determine the output vector by mapping the resulting values. The final output of the ANN is a different percentage of collaborative activities in which learners can participate. This output is used to recommend the activities to the learners. As a testbed for our research, we have developed an intelligent tutoring system for the programming language "Java". Our system was evaluated using an established framework and the statistical hypothesis test. The results show a high degree of pedagogical affordance and usefulness of our approach as well as its positive impact on learning.

Working in groups is an important collaboration activity in the educational context, where a variety of factors can influence the prediction of the teams' performance. In the pertinent bibliography, several machine learning models are available for delivering predictions. In this sense, the main goal of the current research is to assess 28 different machine learning models, including a Deep Neural Network (DNN) which is structured by 4 hidden layers, for predicting teams' performance. Additionally, both data analysis and optimization of input data are also explored for their effectiveness in the improvement of the models' performance. One key finding of the present study is that the XGBoost model succeeded better prediction results, and its precision and robustness were found to be higher, compared to the other models. Additionally, data optimization was shown to be an essential procedure, since the prediction accuracy of all the models, and specifically, that of the XGBoost, improved and found to be 96% during the first phase that of the process, and 94% during the second phase that of the product. Similarly, after applying the hyperparameter tuning and data optimization, the prediction accuracy of the DNN was also improved and found to be 89.94% and 86.16%, during the same two phases. Finally, for interpreting the output of the ML models, in terms of features' importance, the Shapley Additive Explanations framework (SHAP) was used.

The preface to the Proceedings of the 8th Smart Data Smart Cities (SDSC) conference provides an overview of the concept of a Smart City, which involves the use of digital layers for city governance, citizen services, and sustainable development. Smart Cities utilize intelligent technology to enhance urban life by integrating data from various sources such as sensors, demographics, mapping, and building information models. The conference focuses on the use of spatial information and communication technologies in Smart Cities, covering topics such as 3D modeling, citizen engagement, transportation, urban decision-making, and smart urban mobility. The volume consists of 31 research papers selected through blind review, addressing different aspects of Smart Cities. [Extracted from the article]

Nowadays, due to the increasing number of cyberattacks, cybersecurity education, training, and awareness are considered crucial for preparing current and future IT professionals. Thus, it is essential for educational institutions to foster well-designed learning strategies in the field of cybersecurity that will not only focus on theory-based learning interventions but also on encapsulating authentic learning practices. In this context, the paper at hand presents a ubiquitous scenario-based learning (SBL) intervention, blended with IoT devices for introducing the topic of the man-in-the-middle attack to 1st-grade students in vocational education. The learning scenario enables two-way plain text communication through a LoRa network. For securing the transmission and assure confidentiality, basic encryption techniques are enabled for the transmitted messages. Meanwhile, an eavesdropper, acting as the man-in-the-middle attacker, tries to intercept the communication, by applying different decryption techniques. For this purpose, a u-Learning app was developed. The app was evaluated by ninety 1st-grade students of an educational institute of vocational training, in terms of effectiveness, efficiency, knowledge acquisition, and learners' satisfaction. Among others, the results show that the effectiveness and the efficiency of the proposed learning process were 92.03%, and 89.63%, respectively. Finally, learners' satisfaction was high, and their knowledge acquisition was improved. [ABSTRACT FROM AUTHOR]

Computer-Supported Collaborative Learning is a promising innovation that ameliorates tutoring through modern technologies. However, the way of recommending collaborative activities to learners, by taking into account their learning needs and preferences, is an important issue of increasing interest. In this context, this paper presents a framework for providing recommendations of collaborative activities to learners by employing an Artificial Neural Network (ANN) and the Weighted Sum Model (WSM). In our approach, the Gardner and Korth framework is employed to identify the dimensions of the learners' collaborative learning styles. For each dimension, different weights are assigned and used as input data for the ANN. Then, the WSM and the activation function determine the output vector by mapping the resulting values. The final output of the ANN is a different percentage of collaborative activities in which learners can participate. This output is used to recommend the activities to the learners. As a testbed for our research, we have developed an intelligent tutoring system for the programming language "Java". Our system was evaluated using an established framework and the statistical hypothesis test. The results show a high degree of pedagogical affordance and usefulness of our approach as well as its positive impact on learning. [ABSTRACT FROM AUTHOR]

An input-vector monitoring concurrent Built-In Self-Test (BIST) technique for combinational circuits that is based on a self-testing Random Access Memory (RAM) is discussed. RAM-based Input Vector Monitoring Concurrent BIST (R-CBIST) is recommended for testing Read Only Memory (ROM) as it results in small hardware overhead without stopping the normal operation of ROM.