Framing professional competencies for systems thinking in practice: final report of an action research eSTEeM inquiry

The Open University eSTEeM project (The OU Centre for STEM Pedagogy) was a 12-month inquiry beginning March 2017 building on an initial eSTEeM project (2014-2016) entitled ‘Enhancing Systems Thinking in Practice in the Workplace’ reported on in Reynolds et al (2016). The initial report highlighted the challenges of enacting systems thinking in practice (STiP) in the workplace after qualifying with STiP core modules at The OU. Expressions of interest were manifest amongst systems thinking practitioners and employers for having some kind of formalised externally validated ‘competency framework’ for professional recognition of systems thinking in practice. The primary aim of the inquiry was to provide STiP alumni with externally recognised institutionalised professional backing for their newly acquired skill-sets associated with systems thinking. The project aimed to design a learning system – through the idea of an action learning lab – for developing a competency framework associated with systems thinking in practice. The project was carried out by a core team of three academics – Reynolds, Shah, and van Ameijde, associated with the Applied Systems Thinking in Practice (ASTiP) Group in the School of Engineering and Innovation, along with advice and support from other ASTiP colleagues – most notably Ray Ison and Chris Blackmore. The inquiry comprised some desktop research on competency framings, a series of online interviews, the drafting of an interim report, a video recording of employee/ employer interaction regarding application of STiP competencies in the workplace, a workshop held in London Regional Office in June 2017, and follow-up reporting and conversations arising from the workshop. One significant outcome from this activity led to ideas and consultations with Employer representatives, professional bodies and the Institute for Apprenticeships to initiate a Trailblazing Committee for a new Systems Thinking Practitioner apprenticeship Standard

From the classroom to the computer screen: delivering a traditional University course in a non-traditional way

The present Technical Reports contains two complementary papers describing our experience with a system for delivering traditional lectures through computers and computer networks

Formality and informality in the summative assessment of motor vehicle apprentices: a case study

This article explores the interaction of formal and informal attributes of competence‐based assessment. Specifically, it presents evidence from a small qualitative case study of summative assessment practices for competence‐based qualifications within apprenticeships in the motor industry in England. The data are analysed through applying an adaptation of a framework for exploring the interplay of formality and informality in learning. This analysis reveals informal mentoring as a significant element which influences not only the process of assessment, but also its outcomes. We offer different possible interpretations of the data and their analysis, and conclude that, whichever interpretation is adopted, there appears to be a need for greater capacity‐building for assessors at a local level. This could acknowledge a more holistic role for assessors; recognise the importance of assessors’ informal practices in the formal retention and achievement of apprentices; and enhance awareness of inequalities that may be reinforced by both informal and formal attributes of assessment practices

Global citizenship and critical thinking in higher education curricula and police education: a socially critical vocational perspective

The re‐emergence of the concept of global citizenship within higher education (HE) after what Smith et al. (2008, p.136) have described as ‘many years of comparative neglect’ has reopened the debate about the fundamental roles, responsibilities and purpose of HE. Rhoads and Szelenyi (2011, p8‐9) argue that not only do ‘universities have an obligation to use their knowledge capacities to advance social life and to better the human condition’, but they also have a responsibility for ‘advancing global social relations’. Likewise, Camicia and Franklin (2011, p.39) maintain that universities have the ‘intellectual authority that society needs to help it reflect, understand and act’ which suggests that Higher Education Institutions (HEIs) have a profound and moral responsibility to take a leading and active role in creating a more enlightened, socially just and civilised global society

Criminal intent or cognitive dissonance: how does student self plagiarism fit into academic integrity?

The discourse of plagiarism is speckled with punitive terms not out of place in a police officer's notes: detection, prevention, misconduct, rules, regulations, conventions, transgression, consequences, deter, trap, etc. This crime and punishment paradigm tends to be the norm in academic settings. The learning and teaching paradigm assumes that students are not filled with criminal intent, but rather are confused by the novel academic culture and its values. The discourse of learning and teaching includes: development, guidance, acknowledge, scholarly practice, communicate, familiarity, culture. Depending on the paradigm adopted, universities, teachers, and students will either focus on policies, punishments, and ways to cheat the system or on program design, assessments, and assimilating the values of academia. Self plagiarism is a pivotal issue that polarises these two paradigms. Viewed from a crime and punishment paradigm, self plagiarism is an intentional act of evading the required workload for a course by re-using previous work. Within a learning and teaching paradigm, self plagiarism is an oxymoron. We would like to explore the differences between these two paradigms by using self plagiarism as a focal point

Societal Effects and the Transfer of Business Practices to Britain and France

This paper seeks to reconcile the notion of a 'societal effect' in business organisation with the considerable evidence that competitive pressures continuously lead national producers to emulate the business practices of other nations, which are perceived as providing a basis for superior economic performance. The paper identifies three sources of national specificity in the process of emulation giving rise to 'hybrid' models. First, the fact that a nation's manufacturers have a distinctive knowledge base means that adopting another nation's methods will depend on local learning involving trial and error. The more 'distant' the emulated technology is from the local one, the less likely it is that this learning process will result in an exact replica of the parent model. Second, when there are strong interdependencies between a nation's production methods and its systems of vocational training, there will be strong pressure to adopt new methods in ways that are compatible with existing career structures. Third, the fact each nation has a particular industrial relations legacy involving varying levels of trust between labour and management, means that new practices will be introduced through a distinctive process of negotiation and compromise giving rise to national specific effects.knowledge, learning processes, national specificity

Using machine learning to learn from demonstration: application to the AR.Drone quadrotor control

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. December 14, 2015Developing a robot that can operate autonomously is an active area in robotics research. An autonomously operating robot can have a tremendous number of applications such as: surveillance and inspection; search and rescue; and operating in hazardous environments. Reinforcement learning, a branch of machine learning, provides an attractive framework for developing robust control algorithms since it is less demanding in terms of both knowledge and programming effort. Given a reward function, reinforcement learning employs a trial-and-error concept to make an agent learn. It is computationally intractable in practice for an agent to learn “de novo”, thus it is important to provide the learning system with “a priori” knowledge. Such prior knowledge would be in the form of demonstrations performed by the teacher. However, prior knowledge does not necessarily guarantee that the agent will perform well. The performance of the agent usually depends on the reward function, since the reward function describes the formal specification of the control task. However, problems arise with complex reward function that are difficult to specify manually. In order to address these problems, apprenticeship learning via inverse reinforcement learning is used. Apprenticeship learning via inverse reinforcement learning can be used to extract a reward function from the set of demonstrations so that the agent can optimise its performance with respect to that reward function. In this research, a flight controller for the Ar.Drone quadrotor was created using a reinforcement learning algorithm and function approximators with some prior knowledge. The agent was able to perform a manoeuvre that is similar to the one demonstrated by the teacher

Learning Sciences for Computing Education

his chapter discusses potential and current overlaps between the learning sciences and computing education research in their origins, theory, and methodology. After an introduction to learning sciences, the chapter describes how both learning sciences and computing education research developed as distinct fields from cognitive science. Despite common roots and common goals, the authors argue that the two fields are less integrated than they should be and recommend theories and methodologies from the learning sciences that could be used more widely in computing education research. The chapter selects for discussion one general learning theory from each of cognition (constructivism), instructional design (cognitive apprenticeship), social and environmental features of learning environments (sociocultural theory), and motivation (expectancy-value theory). Then the chapter describes methodology for design-based research to apply and test learning theories in authentic learning environments. The chapter emphasizes the alignment between design-based research and current research practices in computing education. Finally, the chapter discusses the four stages of learning sciences projects. Examples from computing education research are given for each stage to illustrate the shared goals and methods of the two fields and to argue for more integration between them