DSpace Collection:
https://hdl.handle.net/2440/1078
2024-03-29T01:43:13ZQuality in blended learning environments – Significant differences in how students approach learning collaborations
https://hdl.handle.net/2440/140359
Title: Quality in blended learning environments – Significant differences in how students approach learning collaborations
Author: Ellis, R.A.; Pardo, A.; Han, F.
Abstract: Evaluating the quality of student experiences of learning in a blended environment requires the careful consideration of many aspects that can contribute to learning outcomes. In this study, university students in first year engineering were required to collaborate and inquire in a blended course design over a semester-long course. This study investigates their approaches to inquiry and online learning technologies as they collaborated both in class and online. The results identify sub-groups within the population sample (n > 200) which reported qualitatively different experiences of how they approached inquiry and used the online learning technologies. The results also measure aspects of their collaborations which help to explain why some students were more successful than others. The outcomes of the study have important implications for teaching and course design and the effective evaluation of blended experiences of university student learning.2016-01-01T00:00:00ZThin film extensional flow of a transversely isotropic viscous fluid
https://hdl.handle.net/2440/140318
Title: Thin film extensional flow of a transversely isotropic viscous fluid
Author: Hopwood, M.J.; Harding, B.; Green, J.E.F.; Dyson, R.J.
Abstract: Many biological materials such as cervical mucus and collagen gel possess a fibrous microstructure. This microstructure affects the emergent mechanical properties of the material and hence the functional behavior of the system. We consider the canonical problem of stretching a thin sheet of transversely isotropic viscous fluid as a simplified version of the spinnbarkeit test for cervical mucus. We propose a solution to the model constructed by Green and Friedman by manipulating the model to a form amenable to arbitrary Lagrangian-Eulerian (ALE) techniques. The system of equations, reduced by exploiting the slender nature of the sheet, is solved numerically, and we discover that the bulk properties of the sheet are controlled by an effective viscosity dependent on the evolving angle of the fibers. In addition, we confirm a previous conjecture by demonstrating that the center line of the sheet need not be flat, and perform a short timescale analysis to capture the full behavior of the center line.2023-01-01T00:00:00ZAnalytics for learning design: A layered framework and tools
https://hdl.handle.net/2440/140055
Title: Analytics for learning design: A layered framework and tools
Author: Hernández-Leo, D.; Martinez-Maldonado, R.; Pardo, A.; Muñoz-Cristóbal, J.A.; Rodríguez-Triana, M.J.
Abstract: The field of learning design studies how to support teachers in devising suitable activities for their students to learn. The field of learning analytics explores how data about students’ interactions can be used to increase the understanding of learning experiences. Despite its clear synergy, there is only limited and fragmented work exploring the active role that data analytics can play in supporting design for learning. This paper builds on previous research to propose a framework (analytics layers for learning design) that articulates three layers of data analytics—learning analytics, design analytics and community analytics—to support informed decision-making in learning design. Additionally, a set of tools and experiences are described to illustrate how the different data analytics perspectives proposed by the framework can support learning design processes.2019-01-01T00:00:00ZToward a Distributed Trust Management System for Misbehavior Detection in the Internet of Vehicles
https://hdl.handle.net/2440/140037
Title: Toward a Distributed Trust Management System for Misbehavior Detection in the Internet of Vehicles
Author: Mahmood, A.; Sheng, Q.Z.; Zhang, W.E.; Wang, Y.; Sagar, S.
Abstract: Recent considerable state-of-the-art advancements within the automotive sector, coupled with an evolution of the promising paradigms of vehicle-to-everything communication and the Internet of Vehicles (IoV), have facilitated vehicles to generate and, accordingly, disseminate an enormous amount of safety-critical and non-safety infotainment data in a bid to guarantee a highly safe, convenient, and congestion-aware road transport. These dynamic networks require intelligent security measures to ensure that the malicious messages, along with the vehicles that disseminate them, are identified and subsequently eliminated in a timely manner so that they are not in a position to harm other vehicles. Failing to do so could jeopardize the entire network, leading to fatalities and injuries amongst road users. Several researchers, over the years, have envisaged conventional cryptographic-based solutions employing certificates and the public key infrastructure for enhancing the security of vehicular networks. Nevertheless, cryptographic-based solutions are not optimum for an IoV network primarily, since the cryptographic schemes could be susceptible to compromised trust authorities and insider attacks that are highly deceptive in nature and cannot be noticed immediately and are, therefore, capable of causing catastrophic damage. Accordingly, in this article, a distributed trust management system has been proposed that ascertains the trust of all the reputation segments within an IoV network. The envisaged system takes into consideration the salient characteristics of familiarity, i.e., assessed via a subjective logic approach, similarity, and timeliness to ascertain the weights of all the reputation segments. Furthermore, an intelligent trust threshold mechanism has been developed for the identification and eviction of the misbehaving vehicles. The experimental results suggest the advantages of our proposed IoV-based trust management system in terms of optimizing the misbehavior detection and its resilience to various sorts of attacks.2023-01-01T00:00:00Z