E-Learning Strategy Development for Fully Aligned College Curricula

The background of this study is a well-established 21st century trend, leading education institutions towards diversifying their offer with e-learning. While top engineering institutions worldwide have developed e-learning, many others were still focusing on limited traditional offers until the COVID-19 pandemic of 2020 which has brought unprecedented upheaval and drastic change even for small local colleges. The aim of this study is to analyse the dimensions of this change and evaluate the change management processes across engineering departments in local UK colleges that have had to shift their teaching, learning and assessment from face-to-face delivery to an e-environment in the context of the COVID-19 pandemic, and are considering moving onto e-learning wherever possible. The initial research for this study has been collected across 77 further education colleges in the North West of the UK that also have a higher education offer and that teach engineering at various levels. Key aspects of the move to e-learning as well as the wider context will be presented, followed by a contextualised literature review where examples of challenges from analysed institutions will be presented in relation to key concepts of change management. Some critical aspects of higher education across colleges are considered along with their challenges in the present COVID-19 context. This will be followed by recommendations. The basis for the recommendations is an extended market and change management analysis available in the appendices to this work. SWOT, Porter’s Five Forces as well as stakeholder analyses are complemented by Culture Web and McKinsey’s 7S models which have been used to analyse the current state of operations and present strategic positioning of UK colleges with an engineering further and higher education provision in order to identify which of these may need reinforcement and propose recommendations. Keywords: e-learning, higher education, strategy, colleges, engineering education DOI: 10.7176/JEP/11-36-01 Publication date: December 31st 202

Improving the Learning Environment Through Communication: A Systems Perspective

The call centre can be best described as an organizational system with three sub-systems made up of 15 sections. Systems are impacted by their environment and respond by outputting their own impact onto the environment (Jones, 2013; Kast & Rosenzweig, 2017). In the case of the call centre’s system, the environment is the caller (client), and the impact is the increased complexity in the types of questions asked. The call centre was impacted by clients who posed complex questions, and the system responded by outputting incorrect information to its clients (Internal Report, 2015). Internal research revealed that the system was unable to prepare its parts (sub-systems and sections) for the changes in the types of questions asked by clients (Internal Report, 2015). A solution was devised to transform the call centre into a system that is prepared to output correct information when impacted by changes to question types. The solution focuses on changing the formal learning process because its function is to prepare the system for outputting correct information. This organizational improvement plan (OIP) uses a transformational leadership approach to identify the problem, define the desired end-state, outline a strategy, and communicate the change plan

Adaptation strategies for self-organising electronic institutions

For large-scale systems and networks embedded in highly dynamic, volatile, and unpredictable environments, self-adaptive and self-organising (SASO) algorithms have been proposed as solutions to the problems introduced by this dynamism, volatility, and unpredictability. In open systems it cannot be guaranteed that an adaptive mechanism that works well in isolation will work well — or at all — in combination with others. In complexity science the emergence of systemic, or macro-level, properties from individual, or micro-level, interactions is addressed through mathematical modelling and simulation. Intermediate meso-level structuration has been proposed as a method for controlling the macro-level system outcomes, through the study of how the application of certain policies, or norms, can affect adaptation and organisation at various levels of the system. In this context, this thesis describes the specification and implementation of an adaptive affective anticipatory agent model for the individual micro level, and a self-organising distributed institutional consensus algorithm for the group meso level. Situated in an intelligent transportation system, the agent model represents an adaptive decision-making system for safe driving, and the consensus algorithm allows the vehicles to self-organise agreement on values necessary for the maintenance of “platoons” of vehicles travelling down a motorway. Experiments were performed using each mechanism in isolation to demonstrate its effectiveness. A computational testbed has been built on a multi-agent simulator to examine the interaction between the two given adaptation mechanisms. Experiments involving various differing combinations of the mechanisms are performed, and the effect of these combinations on the macro-level system properties is measured. Both beneficial and pernicious interactions are observed; the experimental results are analysed in an attempt to understand these interactions. The analysis is performed through a formalism which enables the causes for the various interactions to be understood. The formalism takes into account the methods by which the SASO mechanisms are composed, at what level of the system they operate, on which parts of the system they operate, and how they interact with the population of the system. It is suggested that this formalism could serve as the starting point for an analytic method and experimental tools for a future systems theory of adaptation.Open Acces