A deep learning classification framework for early prediction of team-based academic performance

Giannakas, Filippos; Troussas, Christos; Voyiatzis, Ioannis; Sgouropoulou, Cleo

doi:10.1016/j.asoc.2021.107355

Cited by 52 publications

(50 citation statements)

References 37 publications

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“…Therefore, artificial neural networks (ANN) can be conceived as a machine learning methodology for defining the function (metamodel) that connects a set of inputs to a set of outputs. In the relevant literature [12,13], ANNs have been used in a number of studies.…”

Section: Metamodel Of Interpreting Students' Satisfactionmentioning

confidence: 99%

Creating a Metamodel for Predicting Learners’ Satisfaction by Utilizing an Educational Information System During COVID-19 Pandemic

Kanetaki

Stergiou

Bekas

et al. 2021

Frontiers in Artificial Intelligence and Applications

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Faced with the disruption generated by the COVID-19 pandemic, the advent of enforced and exclusive online learning presented a challenging opportunity for researchers worldwide, to quickly adapt curricula to this new reality and gather electronic data by tracking students’ satisfaction after attending online modules. Many researchers have looked into the subject of student satisfaction to discover if there is a link between personal satisfaction and academic achievement. Using a set of data, filtered out of a statistical analysis applied on an online survey, with 129 variables, this study investigates students’ satisfaction prediction in a first-semester Mechanical Engineering CAD module combined with the evaluation and the effectiveness of specific curriculum reforms. A hybrid machine learning model that has been created, initially consists of a Generalized Linear Model (GLAR), based on critical variables that have been filtered out after a correlation analysis. Its fitting errors are utilized as an extra predictor, that is used as an input to an artificial neural network. The model has been trained using as a basis the 70% of the population (consisting of 165 observations) to predict the satisfaction of the remaining 30%. After several trials and gradual improvement, the metamodel’s architecture is produced. The trained hybrid model’s final form had a coefficient of determination equal to 1 (R = 1). This indicates that the data fitting method was successful in linking the independent variables with the dependent variable 100 percent of the time (satisfaction prediction).

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Section: Metamodel Of Interpreting Students' Satisfactionmentioning

confidence: 99%

Creating a Metamodel for Predicting Learners’ Satisfaction by Utilizing an Educational Information System During COVID-19 Pandemic

Kanetaki

Stergiou

Bekas

et al. 2021

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

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“…Moreover, to better diagnose, forecast and characterize individuals' health, several models exploit machine learning in healthcare, as discussed in refs. [16][17][18].…”

Section: Related Workmentioning

confidence: 99%

A Network Architecture and Routing Protocol for the MEDIcal WARNing System

Leonardi

Bello

Patti

et al. 2021

JSAN

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The MEDIcal WARNing (MEDIWARN) system continuously and automatically monitors the vital parameters of pre-intensive care hospitalized patients and, thanks to an intelligent processing system, provides the medical teams with a better understanding of their patients’ clinical condition, thus enabling a prompt reaction to any change. Since the hospital units generally lack a wired infrastructure, a wireless network is required to collect sensor data in a server for processing purposes. This work presents the MEDIWARN communication system, addressing both the network architecture and a simple, lightweight and configurable routing protocol that fits the system requirements, such as the ability to offer path redundancy and mobility support without significantly increasing the network workload and latency. The novel protocol, called the MultiPath Routing Protocol for MEDIWARN (MP-RPM), was therefore designed as a solution to support low-latency reliable transmissions on a dynamic network while limiting the network overhead due to the control messages. The paper describes the MEDIWARN communication system and addresses the experimental performance evaluation of an implementation in a real use-case scenario. Moreover, the work discusses a simulative assessment of the MEDIWARN communication system performance obtained using different routing protocols. In particular, the timeliness and reliability results obtained by the MP-RPM routing protocol are compared with those obtained by two widely adopted routing protocols, i.e., the Ad-hoc On-demand Distance Vector (AODV) and the Destination-Sequenced Distance-Vector Routing (DSDV).

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“…New technologies familiar from the Smart City and Home as intelligent products and packaging, radiofrequency identification (RFID), we find them in Smart Products and in their packaging replacing the traditional packaging. The new intelligent packaging incorporates microchips, antennas and new materials such as thermochromic inks and various indicators that track changes and provide real-time responses and wireless communication capabilities [26]. From the literature packaging is defined as the mean to protect the product from environmental changes and distribution, with some graphical information in a way to communicate with the consumer and provide product information.…”

Section: What Is Smart Intelligent a Package Paradigm From The Conceptual Model To The Smart And To The Intelligentmentioning

confidence: 99%

“…Intelligent packaging has a communicative character with printed electronics and the existence of microchips, antennas and batteries. RFID tags provide a product description and allow content identification [26].…”

Section: What Is Smart Intelligent a Package Paradigm From The Conceptual Model To The Smart And To The Intelligentmentioning

confidence: 99%

Ambient Intelligence and Smart Environments: A Preliminary Overview

Poli

2021

Frontiers in Artificial Intelligence and Applications

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Nowadays human activities are incoming at a digitalization stage. The introduction of information technology along with new forms of communication, influence a variety of forms of human action and focus mainly on the integration and the convergence of the digital and physical worlds. The use of more intelligent – electronic solutions, improves the lives of people around the world, according to studies carried out on the ingress of new smart technologies. Artificial and Ambient intelligence nowadays getting more and more attention about the development of smart, digital environments. The Smart Cities designed for All must aim to arrange the disparity in cities through smart technology, making cities both smart and accessible to a range of users regardless of their abilities or disabilities. The birth of “Artificial Intelligence” (AI) has facilitated the complex computations for reality simulation the new communication era of wireless 5G, all combined have given the hope for a new and better future, to reverse disability to empower the humans with more capabilities, to be faster than they can ever be, stronger than they can ever dream. This paper provides an overview of Ambient Intelligence and smart environments, as well as how technological advancements will benefit everyday usage by devices in common spaces such as homes or offices, and how they will interact and serve as a part of an intelligent ecosystem by bringing together resources such as networks, sensors, human-computer interfaces, pervasive computing, and so on.

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A deep learning classification framework for early prediction of team-based academic performance

Cited by 52 publications

References 37 publications

Creating a Metamodel for Predicting Learners’ Satisfaction by Utilizing an Educational Information System During COVID-19 Pandemic

Creating a Metamodel for Predicting Learners’ Satisfaction by Utilizing an Educational Information System During COVID-19 Pandemic

A Network Architecture and Routing Protocol for the MEDIcal WARNing System

Ambient Intelligence and Smart Environments: A Preliminary Overview

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