Dr. Tryfon Sivenas
Department of Pedagogy and Primary Education, National and Kapodistrian University of Athens, Athens, Attikí, Greece
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Academic Career Length
5Years (2022-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications4 February 2025 in Transportation Infrastructure Geotechnology
Journal Article
ITIshwor Thapa
Ishwor ThapaSGSufyan Ghani
Sufyan GhaniSKSunita Kumari
Sunita KumariACA. K. Choudhary
A. K. ChoudharyTryfon Sivenas
Panagiotis G. AsterisThis study presents a novel approach to predicting the factor of safety (FOS) for infinite slopes stabilized with nano-silica (NS), leveraging machine learning (ML) models to address limitations of traditional geotechnical assessments. A unique dataset, consisting of 1053 samples, was compiled to capture critical parameters, including NS content and curing time, thereby enhancing model accuracy across diverse conditions. Six ML models were evaluated, with the gradient boosting (GB) model emerging as the most robust, achieving an R 2 value of 0.99, mean absolute error of 0.03 and root mean squared error of 0.04. A reliability analysis quantified a failure probability of 29.66% and a reliability index of 1.64 for NS-treated soils, underscoring the model’s applicability in real-world geotechnical design. Additionally, a parabolic regression equation was derived, offering practitioners a reliable tool for FOS estimation. To facilitate practical implementation, an intuitive graphical user interface (GUI) was developed, allowing for accurate FOS predictions based on user-defined inputs. This study provides a comprehensive, data-driven model for NS-stabilized slopes, advancing the field with a user-friendly predictive tool that supports sustainable engineering practices in challenging geotechnical environments.
1 July 2022
Conference Paper
Tryfon SivenasGKGeorge Koutromanos
George Koutromanos
The aim of this study was the examination of the perceived affordances and constraints of drones for teaching and learning. The sample consisted of 44 in-service teachers who attended an introductory presentation on drones, their technology and control mechanisms and afterwards assembled, flew and programmed four multicopter drones. Data was collected through anonymous online questionnaires. Results from qualitative data analysis revealed seven affordances, namely programming through block-based languages, recording and bird’s-eye view, viewing places that are unseen from ground level and real-time photo and video streaming, drone assembly, data collection and processing, gamification, and development of several student skills. Additionally, they revealed four constraints, namely the necessity of teacher training, time restrictions regarding the drone’s battery life, infrastructural and personal data restrictions. These findings will contribute to a better understanding of the educational value of drones for teaching and learning and, at the same time, provide a base-layer for future research in using drones for educational purposes.
30 May 2022
Conference Paper
Tryfon SivenasGKGeorge Koutromanos
George Koutromanos
TMTassos A. Mikropoulos
Tassos A. Mikropoulos
This study examined in-service teachers’ perceptions regarding spatial presence, simulator sickness and usability of First-Person view drones through Head-Mounted Displays in order to determine the suitability of their use in teaching and learning processes. The sample consisted of 60 in-service teachers of primary education. Data was collected via the Temple Presence Inventory Scale, Simulator Sickness Scale and the System Usability Scale. Results showed that the teachers rated an increased level of spatial presence. Additionally, the simulator sickness was relatively low, and the drone’s usability was rated as excellent. These findings contribute to the better understanding of the potential of First-Person view drones as learning tools.
31 October 2023
Book Chapter
GKGeorge Koutromanos
George KoutromanosIVIoannis Vrellis
Ioannis VrellisTMTassos A. Mikropoulos
Tassos A. MikropoulosTryfon SivenasAbout Me
Dr. Tryfon Sivenas is a Postdoctoral Researcher in the Department of Pedagogy and Primary Education at the National and Kapodistrian University of Athens (NKUA), where he heads the Aerial Robotics unit of the Informatics Laboratory and teaches in the MEd programmes in STEM Education and Educational Robotics and in Mathematics Education. He is Adjunct Lecturer at the University of the Peloponnese and Visiting Lecturer at the Czech Technical University in Prague (CVUT), and has taught undergraduate courses in technology and engineering education at the School of Pedagogical and Technological Education (ASPETE). His research focuses on generative AI in education and AI literacy, educational drone robotics and human-drone interaction in learning contexts, and extended reality in learning. He holds a Ph.D. in Aerial Robotics from NKUA, an M.Sc. in Telecommunication Networks and Telematics from Harokopio University of Athens, an M.Ed. in Special Education from the University of Patras, and a B.Sc. in Computer Science from the University of Ioannina. He is a member of the Erasmus+ AI Academy project team, Finance Chair of the Immersive Learning Research Network (iLRN), editorial board member of SAGE Open (Education section), and reviewer for journals in educational technology and intelligent systems. Earlier, he collaborated with Fortune 500 technology companies, including Microsoft, on projects in cybersecurity and blockchain analysis.
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