Artificial Intelligence Empowers Gamification: Optimizing Student Engagement and Learning Outcomes in E-learning and MOOCs

In this era of Artificial Intelligence (AI) growth, characterized by advances in the Large Language Models (LLMs) used by ChatGPT and Bard, this study examines the effects of gamification and Automatic Question Generation (AQG) on student engagement and learning outcomes in the context of a Massive Open Online Course (MOOC). AQG, implemented via a Moodle plugin, transforms conventional assessments into an interactive, gamified experience, leveraging the “test effect” to improve learning outcomes. Research with 100 fifth-graders in a primary and secondary school shows that gamified assessments significantly boost student motivation and learning outcomes compared with traditional methods. The custom Moodle plugin facilitates the AQG process, generating contextually relevant and grammatically correct Multiple-Choice Questions (MCQs) from course content. The result is a dynamic, personalized assessment experience aimed at optimizing student retention. This paper concludes by discussing the implications of the study for educators and highlighting potential directions for future research

Fuzzy Logic based Intrusion Detection System against Black Hole Attack in Mobile Ad Hoc Networks

A Mobile Ad hoc NETwork (MANET) is a group of mobile nodes that rely on wireless network interfaces, without the use of fixed infrastructure or centralized administration. In this respect, these networks are very susceptible to numerous attacks. One of these attacks is the black hole attack and it is considered as one of the most affected kind on MANET. Consequently, the use of an Intrusion Detection System (IDS) has a major importance in the MANET protection. In this paper, a new scheme has been proposed by using an Adaptive Neuro Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) for mobile ad hoc networks to detect the black hole attack of the current activities. Evaluations using extracted database from a simulated network using the Network Simulator NS2 demonstrate the effectiveness of our approach, in comparison to an optimized IDS based ANFIS-GA

Artificial Intelligence System in Aid of Pedagogical Engineering for Knowledge Assessment on MOOC Platforms: Open EdX and Moodle

The aim of this research is to provide a novel educational model with the goal of reducing the expenses associated with manual question production and meeting the demand for a continual supply of new questions on MOOC platforms such as Moodle or Open EDX. We considered integrating machine-learning methods with natural language processing in order to increase the number and validity of assessing questions. To accomplish this, we developed a system that generates multilingual questions automatically. Various kinds of evaluation were conducted with two factors in mind: evaluating MOOC learners' competency and the similarity of the generated questions to those created by humans. The first evaluation is based on subjective judgment by three MOOC creators, while the second is based on replies from MOOC participants on machine-generated and human-created questions. Both evaluations revealed that the machine-generated questions performed on par with the human-created questions in terms of evaluating skills and similarity. Moreover, the results demonstrate that most of the produced questions (up to 82 percent) enhance e-assessment when the new suggested technology is used

Improving Performance of Mobile Ad Hoc Network Using Clustering Schemes

Mobile ad hoc network become nowadays more and more used in different domains, due to its flexibility and low cost of deployment. However, this kind of network still suffering from several problems as the lack of resources. Many solutions are proposed to face these problems, among these solutions there is the clustering approach. This approach tries to partition the network into a virtual group. It is considered as a primordial solution that aims to enhance the performance of the total network, and makes it possible to guarantee basic levels of system performance. In this paper, we study some schemes of clustering such as Dominating-Set-based clustering, Energy-efficient clustering, Low-maintenance clustering, Load-balancing clustering, and Combined-metrics based clustering