Understanding how students use and appreciate online resources in the teaching laboratory

The internet is a great resource student\u27s use for learning. Reasons include the ease in searching with sites such as Google, or the vast collection of informative videos on YouTube. The teaching laboratory can also benefit from online resources, especially when students are deficient in prerequisite knowledge. The benefits are greatest when there are non-standard learning paths, and multiple entry points into a degree. This study undertakes a mixed methods research approach to try and understand how students use and appreciate an online resource, called the Training Laboratory, designed to support learning in the engineering teaching laboratory. The targeted resources are used to help support students as well as the laboratory teaching assistants (called laboratory demonstrators). The study finds that such resources are used by a substantial number of students to aid learning, increasing productivity, and improving teaching. The availability of such targeted resources leads to an improved student experience

Training laboratory: Using online resources to enhance the laboratory learning experience

2014 IEEE. Technology has enabled students to search and utilize information from a diverse range of sources. One mechanism that students turn to for additional resources is the internet. This paper explores student interaction with an internet resource, called the Training Laboratory. This resource has multiple uses, including: 1) the training of laboratory teaching assistants; 2) providing students an opportunity to develop pre-requisite laboratory skills; 3) reduce the workload of developing resources when designing laboratory notes; 4) reduce the duplication of learning fundamental laboratory skills in multiple subjects; 5) provide a means to share resources to satellite campuses; and, 6) provide a teaching tool to assist laboratory demonstrators. Feedback from students and staff, three years after implementation, indicate that this is an effective resource that has enhanced learning in the laboratory

Simulación de prácticas de ingeniería con realidad virtual inmersiva: ensayo de dureza

Memoria ID-139. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2018-2019

Automated privacy negotiations with preference uncertainty

Many service providers require permissions to access privacy-sensitive data that are not necessary for their core functionality. To support users’ privacy management, we propose a novel agent-based negotiation framework to negotiate privacy permissions between users and service providers using a new multi-issue alternating-offer protocol based on exchanges of partial and complete offers. Additionally, we introduce a novel approach to learning users’ preferences in negotiation and present two variants of this approach: one variant personalised to each individual user, and one personalised depending on the user’s privacy type. To evaluate them, we perform a user study with participants, using an experimental tool installed on the participants’ mobile devices. We compare the take-it-or-leave-it approach, in which users are required to accept all permissions requested by a service, to negotiation, which respects their preferences. Our results show that users share personal data 2.5 times more often when they are able to negotiate while maintaining the same level of decision regret. Moreover, negotiation can be less mentally demanding than the take-it-or-leave-it approach and it allows users to align their privacy choices with their preferences. Finally, our findings provide insight into users’ data sharing strategies to guide the future of automated and negotiable privacy management mechanisms

Intelligent performance assessment in a virtual electronic laboratory

Laboratory work, in the undergraduate engineering course, is aimed at enhancing students’ understanding of taught concepts and integrating theory and practice. This demands that laboratory work is synchronised with lectures in order to maximise its derivable learning outcomes, measurable through assessment. The typical high costs of raditional engineering laboratory, which often militates against its increased use and the synchronisation of laboratory and lectures, have, in addition to other factors, catalysed the increased adoption of virtual laboratories as a complement to the traditional engineering laboratory. In extreme cases, virtual laboratories could serve as alternative means of providing, albeit simulated, meaningful practical experiences. A Virtual Electronic Laboratory (VEL), which can be used to undertake a range of undergraduate electronic engineering curriculum-based laboratory activities, in a realistic manner, has been implemented as part of the work presented in this thesis. The VEL incorporates a Bayesian Network (BN)-based model for the performance assessment of students’ laboratory work in the VEL. Detailed descriptions of the VEL and the assessment model are given. The evaluation of the entire system is in two phases: evaluation of the VEL as a tool for facilitating students’ deeper understanding of fundamental engineering concepts taught in lectures; and evaluation of the assessment model within the context of the VEL environment. The VEL is evaluated at two different engineering faculties, in two separate universities. Results from the evaluation of the VEL show the effectiveness of the VEL to enhance students’ learning, in the light of appropriate learning scenarios, and provide evidence and support for the use of virtual laboratories in the engineering educational context. Performance data, extracted from students’ behaviour logs (captured and recorded during the evaluation of the VEL) are used to evaluate the assessment model. Results of the evaluation demonstrate the effectiveness of the model as an assessment tool, and the practicability of the performance assessment of students’ laboratory work from their observed behaviour in a virtual learning environment.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Type-2 Fuzzy Logic based Systems for Adaptive Learning and Teaching within Intelligent E-Learning Environments

The recent years have witnessed an increased interest in e-learning platforms that incorporate adaptive learning and teaching systems that enable the creation of adaptive learning environments to suit individual student needs. The efficiency of these adaptive educational systems relies on the methodology used to accurately gather and examine information pertaining to the characteristics and needs of students and relies on the way that information is processed to form an adaptive learning context. The vast majority of existing adaptive educational systems do not learn from the users’ behaviours to create white-box models to handle the high level of uncertainty and that could be easily read and analysed by the lay user. The data generated from interactions, such as teacher–learner or learner–system interactions within asynchronous environments, provide great opportunities to realise more adaptive and intelligent e-learning platforms rather than propose prescribed pedagogy that depends on the idea of a few designers and experts. Another limitation of current adaptive educational systems is that most of the existing systems ignore gauging the students' engagements levels and mapping them to suitable delivery needs which match the students' knowledge and preferred learning styles. It is necessary to estimate the degree of students’ engagement with the course contents. Such feedback is highly important and useful for assessing the teaching quality and adjusting the teaching delivery in small and large-scale online learning platforms. Furthermore, most of the current adaptive educational systems are used within asynchronous e-learning contexts as self-paced e-learning products in which learners can study in their own time and at their own speed, totally ignorant of synchronous e-learning settings of teacher-led delivery of the learning material over a communication tool in real time. This thesis presents novel theoretical and practical architectures based on computationally lightweight T2FLSs for lifelong learning and adaptation of learners’ and teachers’ behaviours in small- and large-scale asynchronous and synchronous e-learning platforms. In small-scale asynchronous and synchronous e-learning platforms, the presented architecture augments an engagement estimate system using a noncontact, low-cost, and multiuser support 3D sensor Kinect (v2). This is able to capture reliable features including head pose direction and hybrid features of facial expression to enable convenient and robust estimation of engagement in small-scale online and onsite learning in an unconstrained and natural environment in which users are allowed to act freely and move without restrictions. We will present unique real-world experiments in large and small-scale e-learning platforms carried out by 1,916 users from King Abdul-Aziz and Essex universities in Saudi Arabia and the UK over the course of teaching Excel and PowerPoint in which the type 2 system is learnt and adapted to student and teacher behaviour. The type-2 fuzzy system will be subjected to extended and varied knowledge, engagement, needs, and a high level of uncertainty variation in e-learning environments outperforming the type 1 fuzzy system and non-adaptive version of the system by producing better performance in terms of improved learning, completion rates, and better user engagements