An application of self-assessment of students in mathematics with intelligent decision systems: questionnaire, design and implementation at digital education.

During the last decade an eruptive increase in the demand for intelligent m-learning environments has been observed since instructors in the online academic procedures need to ensure reliability. The research for decision systems seemed inevitable for flexible and effective learning in all levels of education. The prediction of the performance of students during their final exams is considered as a difficult task. In this paper, an application is presented, contributing to an accurate prediction which would assist educators and learning experts in the extraction of useful knowledge for designing learning interventions with enhanced outcomes.

M-learning in the COVID-19 era: physical vs digital class.

Emerging technologies, such as the development of the Internet of Things and the transition to smart cities, and innovative handheld devices have led to big changes in many aspects of our lives, while more changes were imminent. Education is also a sector that has undergone huge changes due to the spreading of those devices. Even at the era of feature phones, it started to become clear that portable devices with access to the internet can be used for learning. The process of learning with the use of mobile phones was then in an early stage, due to the limitations of feature phones. Whereas, with the introduction of smartphones, education is expected to be drastically altered in the future, in most parts of the world. New, radical, and controversial in some cases, approaches have been developed, over the past years, in an effort to implement a mobile learning process in real life conditions. Intelligent tutoring systems have had rapid growth, especially in the COVID-19 era, while a significant increase in online courses via social networks has also been noted. This paper focuses on presenting the most important research parameters of m-learning during the last decade, while it also incorporates a novel empirical study in the domain. The utilization of educational data has been taken into consideration and is presented, aiming at ways to improve human interaction in the digital classroom.

Comulang: towards a collaborative e-learning system that supports student group modeling.

This paper describes an e-learning system that is expected to further enhance the educational process in computer-based tutoring systems by incorporating collaboration between students and work in groups. The resulting system is called "Comulang" while as a test bed for its effectiveness a multiple language learning system is used. Collaboration is supported by a user modeling module that is responsible for the initial creation of student clusters, where, as a next step, working groups of students are created. A machine learning clustering algorithm works towards group formatting, so that co-operations between students from different clusters are attained. One of the resulting system's basic aims is to provide efficient student groups whose limitations and capabilities are well balanced.

M-health: supporting automated diagnosis and electonic health records.

BACKGROUND: Mobile technology has become a part of our everyday life. Mobile services are used in a wide variety of scientific areas including healthcare. As an intersection of computer supported technology and medicine, m-health is expected to bring higher quality in healthcare. A remedy to deter people from neglecting their health issues is providing further and targeted information, while this information is available on the main devices most people use on a regular basis, namely any station or a mobile phone connected to Internet, enabling access to their health status anytime and at any place. RESULTS: The authors present a framework that is built based on mobile health and which, in addition, incorporates a module that is responsible for making diagnoses. To achieve this, we have applied the Analytical Hierarchical Process algorithm (AHP) on the test results, making the system able to infer the presence or not of an illness in the subject. Data to be processed emerge from the corresponding subjects' electronic health records. Through the resulting system, doctors and health companies, which are involved in medical sciences, are offered a sophisticated, powerful tool that provides supplementary diagnoses about their clients by employing their laboratory medical tests. CONCLUSIONS: In this paper a novel computer supported framework is presented, which is targeted basically in the scientific area of mobile health. The incorporated medical diagnosis module and the online presentation of medical tests results may not only facilitate doctors' and medical agencies work and support healthcare in general, but also and most importantly can benefit users by having an analytical picture of their health status at any place and time. Perhaps one of the most challenging targets for this system to reach is to draw individuals' attention and give them motives to be more concerned about their health.