Student transitions to blended learning: an institutional case study

This paper examines the experiences of students transitioning to blended learning in the University of Glasgow as part of the QAA Enhancement Themes work on Student Transitions. We draw here on exploratory, qualitative research to examine the benefits, challenges and skills developed by students during transitions to blended learning as a means of advancing understanding, and informing future curriculum design. Data from home undergraduate and international postgraduate students were collected over two years through focus groups, individual interviews and end-of-course quality assurance surveys. We found that while home/undergraduate and international/postgraduate students have similar transition experiences, international taught postgraduates encounter additional challenges in terms of acclimatising to UK higher education (HE), especially within shorter programmes of study and where pedagogical and language differences exist. The findings are integrated in a conceptual framework highlighting the importance of access, acculturation (attitudes) and attributes (skills) to enable learner autonomy to engage effectively in blended learning. The findings have implications for institutional infrastructure, curriculum design and learner development. Further research is required to collect a larger data set as a means of developing the study’s conceptual framework, in order to better understand and support diverse student transitions to blended learning

Institutional and Student Transitions Into Enhanced Blended Learning

This presentation provides an overview of the ‘Transitions into blended learning’ project, which has focused on three areas: developing an institutional transition framework, researching student experiences, and identifying interventions to support effective transitions. The framework identified external drivers for blended learning, a set of considerations for institutions, and a set of processes to facilitate change involving three stakeholder groups at the heart of the model. The work included learner experience research with students newly engaged in blended learning. This work identified support needs around access (to technology and learning materials), attitudes (towards learning online) and attributes (skills) needed to engage autonomously in blended learning. The institution-wide Enhancement themes team identified a set of interventions or ‘anchor points’ to prevent the institution ‘drifting back’ into purely traditional approaches to learning and teaching. These included the recognition and promotion of good practice through case studies, development of an institutional e-learning framework, and an event to encourage staff and students to share good practice in blended learning. This three-year project was largely led by a PhD student (JA), working with the principal investigator (VHD) and the institutional representative (KG)

Global attractors and extinction dynamics of cyclically competing species

Transitions to absorbing states are of fundamental importance in nonequilibrium physics as well as ecology. In ecology, absorbing states correspond to the extinction of species. We here study the spatial population dynamics of three cyclically interacting species. The interaction scheme comprises both direct competition between species as in the cyclic Lotka-Volterra model, and separated selection and reproduction processes as in the May-Leonard model. We show that the dynamic processes leading to the transient maintenance of biodiversity are closely linked to attractors of the nonlinear dynamics for the overall species' concentrations. The characteristics of these global attractors change qualitatively at certain threshold values of the mobility and depend on the relative strength of the different types of competition between species. They give information about the scaling of extinction times with the system size and thereby the stability of biodiversity. We define an effective free energy as the negative logarithm of the probability to find the system in a specific global state before reaching one of the absorbing states. The global attractors then correspond to minima of this effective energy landscape and determine the most probable values for the species' global concentrations. As in equilibrium thermodynamics, qualitative changes in the effective free energy landscape indicate and characterize the underlying nonequilibrium phase transitions. We provide the complete phase diagrams for the population dynamics and give a comprehensive analysis of the spatio-temporal dynamics and routes to extinction in the respective phases

Development of an institutional framework to guide transitions into enhanced blended learning in higher education

The rapidly changing digital landscape is having a significant influence on learning and teaching. Our study assesses the response of one higher education institution (HEI) to the changing digital landscape and its transition into enhanced blended learning, which seeks to go beyond the early implementation stage to make the most effective use of online learning technologies to enhance the student experience and student learning outcomes. Evidence from a qualitative study comprising 20 semi-structured interviews, informed by a literature review, has resulted in the development of a holistic framework to guide HEIs transitioning into enhanced blended learning. The proposed framework addresses questions relating to the why (change agents), what (institutional considerations), how (organisational preparedness) and who (stakeholders) of transitions into enhanced blended learning. The involvement of all stakeholder groups is essential to a successful institutional transition into enhanced blended learning

A Novel Visuo-Tactile Object Recognition Pipeline using Transformers with Feature Level Fusion

The task of visuo-tactile object recognition is key in enabling robots to interact with humans and their environment in an efficient and effective manner. The differing statistical properties of visual images and tactile time-series data make visuo-tactile fusion non-trivial and complex. This work investigates the usage of Transformers to perform feature level fusion for visuo-tactile data, utilising the Transformer to generate temporal relationships between the visual and tactile data through its self-attention structure. The proposed pipeline is tested on the PHAC-2 dataset, and a complex ablation experiment is completed across a collection of leading activation functions. The proposed pipeline is demonstrated to achieve state-of-the-art accuracy for visuo-tactile object recognition on the PHAC-2 dataset, achieving a 94.3% accuracy when data from two tactile actions are considered

Fast and Accurate Tactile Object Recognition using a Random Convolutional Kernel Transform

The task of tactile object recognition is an ever-evolving research area comprising of the gathering and processing of features related to the physical interaction between a robotic system and an object or material. For a robotic system to be capable of interacting with the real-world, the ability to identify the object it is interacting with in real-time is required. Information about the object is often strongly enhanced using tactile sensing. Recent advancements in time series classifiers have allowed for the accuracy of real-time tactile object recognition to be improved, therefore generating opportunities for enhanced solutions within this field of robotics. In this paper, improvements are proposed to the state-of-the-art time series classifier ROCKET for analysis of tactile data for the purposes of object recognition. A variety of classifier heads are implemented within the ROCKET pipeline; these models are then trained and tested on the PHAC-2 tactile dataset, achieving state-of-the-art performance of 96.3% for single-modality tactile object recognition while only requiring 11 minutes to train

Student transitions to blended learning – challenges and solutions (workshop)

Student transitions into blended learning - challenges and solutions Josephine Adekola, Vicki Dale, Kerr Gardiner, Kate Powell The proposed workshop will focus on the challenges students experience when transitioning into blended learning. This may be at the start of their university education, or during their studies when introduced to a blended course or programme for the first time. The proposed workshop will begin with a short presentation about the experiences of students from the University of Glasgow. Our research has shown that students appreciate the flexibility of blended learning and recognise the skills developed because of their engagement with it. However, it does introduce challenges around perceived interaction with staff, time management and online assessment literacy. These challenges are greater for international students studying in UK higher education for the first time. In the first activity, staff and student participants will be encouraged to consider the particular challenges facing students at their own institutions, in relation to blended learning, before a second small group activity designed to elicit solutions. The workshop will conclude with a summary discussion of key points and a discussion about how potential solutions may be implemented, including how institutions might work together to support students. The findings from the workshop will be summarised as an article for the Enhancement Themes newsletter and incorporated into our student-facing multimedia output

Corner Detection on hexagonal pixel based images

Corner detection is used in many computer vision applications that require fast and efficient feature matching. In addition, hexagonal pixel based images have been recently investigated for image capture and processing due to their ability to represent curved structures that are common in real images better than traditional rectangular pixel based images. Therefore, we present an approach to corner detection on hexagonal images and demonstrate that accuracy is comparable to well-known existing corner detectors applied to rectangular pixel based images