Assessment-driven Learning through Serious Games: Guidance and Effective Outcomes

Evaluation in serious games is an important aspect; it aims to evaluate the good transmission of pedagogical objectives, the performance of student in relation to these objectives defined in the pedagogical scenario, the content of the course and the predefined criteria. However, the effectiveness of learning is under-studied due to the complexity involved to gamify the assessment concept, particularly when it comes to intangible measures related to the progression of learning outcomes, which is among the most important aspects of evaluation in serious games. This paper reviews the literature regarding assessment due to their importance in the learning process with a detailed assessment plan applied on serious game. Then, it presents a framework used to facilitate the assessment design integrated in serious games. Finally, a significant example of how the proposed framework proved successful with corresponding results will conclude the paper

Serious Games Adaptation According to the Learner’s Performances

Basically, serious games provides enjoyment and knowledge, several researches in this field have focused into joining these two proprieties and make the best balance between them, in order, to provide the best game and enjoyable game experience and ensure the learning of the needed knowledge. Players differ and their knowledge background can be a lot different from one to the other. This study focused on how the SG adapts and provide the needed knowledge and enjoyment. The game should analyze players behavior from different angles, thus it can add difficulty, information, immersion or enjoyment modules to fit the player skills/knowledge

Transforming Healthcare: Leveraging Vision-Based Neural Networks for Smart Home Patient Monitoring

Image captioning is a promising technique for remote monitoring of patient behavior, enabling healthcare providers to identify changes in patient routines and conditions. In this study, we explore the use of transformer neural networks for image caption generation from surveillance camera footage, captured at regular intervals of one minute. Our goal is to develop and evaluate a transformer neural network model, trained and tested on the COCO (common objects in context) dataset, for generating captions that describe patient behavior. Furthermore, we will compare our proposed approach with a traditional convolutional neural network (CNN) method to highlight the prominence of our proposed approach. Our findings demonstrate the potential of transformer neural networks in generating natural language descriptions of patient behavior, which can provide valuable insights for healthcare providers. The use of such technology can allow for more efficient monitoring of patients, enabling timely interventions when necessary. Moreover, our study highlights the potential of transformer neural networks in identifying patterns and trends in patient behavior over time, which can aid in developing personalized healthcare plans

A Statistical Multiplexing Method for Traffic Signal Timing Optimization in Smart Cities

Urban road traffic is the heart of many problems: more recent years, this critical aspect involved every day is unfavorable to many fields, such as economics or ecology. For these reasons, the Intelligent Transportation Systems (ITS) have emerged to best optimize the expenditure of the user on often complex road networks. In this paper, after studying the backgrounds of such systems, we propose a system of control of traffic lights through the use of statistical multiplexing technique based on fixed and vehicular networks of wireless sensors. We will see that this architecture can be flexible within the framework of ITS and participate in low cost to obtain interesting results. The simulation results prove the efficiency of the traffic system in an urban area with an adaptable and dynamic traffic road, because the average waiting time of cars at the intersection is sharply dropped when the red light duration is 65 s and the green light time duration is 125 s. DOI: http://dx.doi.org/10.11591/telkomnika.v15i1.8092