No abstract

No abstract

This paper deals with the problem of extracting semantic knowledge in the production of ER models from natural language specifications. The application of semantic heuristics is proposed as the strategy to obtain the relevant ER elements such as entities, attributes and relationships from the specifications. Earlier research has shown that syntactic heuristics produced good results in identifying the relevant and correct results of the ER elements in terms of recall and precision. The inclusion of the semantic lexical knowledge is hoped to further improve the results. The semantic heuristics may later be implemented as part of a natural language tool in the generation of the ER models.

The assurance of quality through degree accreditation by Professional, Statutory and Regulatory Bodies (PSRBs) is very much a feature of higher education in the UK. In this dynamic and emerging UK educational, economic and policy environment, there still remains a need for accreditation regimes to evolve in order to maximise the value they provide to higher education institutions, as well as to industry and society as a whole.The Shadbolt review, an independent review of computer science degree accreditation and graduate employability conducted in 2016, focused on the purpose and role of degree accreditation, how the system can support the skills requirements of employers, and how the system can improve graduate employability. This paper provides an update in the context of one professional body -BCS, The Chartered Institute for IT -of what has happened in response to the recommendations of the Shadbolt review, focusing on ongoing enhancement projects, as well as commentary and recommendations for future activities and initiatives.

There exists a significant gap between the requirements specified within higher education qualifications and the requirements sought by employers. The former, commonly expressed in terms of learning outcomes, provide a measure of capability, of what skills have been learnt (an input measure); the latter, commonly expressed in terms of role descriptions, provide a measure of competency, of what a learner has become skillful in ( an output measure). Accreditation traditionally provides a way of translating and embedding industry-relevant content into education programmes but current approaches make fully addressing this requirements gap, referred to here as the Capability-Competency Chasm, very difficult. This paper explores current efforts to address this global challenge, primarily through STEM examples that apply within the United Kingdom and European Union, before proposing a way of bridging this chasm through the use of a 21 st Century (C21) skills taxonomy. The concept of C21 Skills Hours as a new input measurement for learning within qualifications is introduced, and an illustrative example is presented to show the C21 skills taxonomy in action. The paper concludes with a discussion of how such a taxonomy can also be used to support a microcredentialing framework that aligns to existing competency frameworks, enabling formal, nonformal and informal learning to all be recognized. A C21 Skills taxonomy can therefore be used to bridge the gap between capability (input) and competency (output), providing a common language both for learning and demonstrating a skill. This approach has profound implications for addressing current and future skills gaps as well as for supporting a transition to more personalised learning within schools, colleges and universities and more lifelong learning both during and outside of employment.