Elena Zaitseva – författare

Analysing Student Feedback in Higher Education provides an in-depth analysis of ‘mining’ student feedback that goes beyond numerical measures of student satisfaction or engagement. By including authentic student voices for understanding the student experience, this book will inform strategies for quality improvement in higher education globally.With contributions, representing an international community of academics, educational developers, institutional data analysts and student-researchers, this book reflects on the role of computer-aided text analysis in gaining insight of student views. The chapters explore the applications of text-mining in different forms, these include varied institutional contexts, using a range of instruments and pursuing different institutional aims and objectives. Contributors provide insights enabled by computer-aided analysis in distilling the student voice and turning large volumes of data into useful information and knowledge to inform actions. Practical tips and core principles are explored to assist academic institutions when embarking on analysing qualitative student feedback.Written for a wide audience, Analysing Student Feedback in Higher Education provides those making informed decisions about how to approach analyses of large volumes of student narratives, with the benefit of learning from the experiences of those who already started treading this path. It enables academic developers, institutional researchers, academics, and administrators to see how bringing text mining to their institutions can help them in better understanding and using the student voice to improve practice.

Analysing Student Feedback in Higher Education provides an in-depth analysis of ‘mining’ student feedback that goes beyond numerical measures of student satisfaction or engagement. By including authentic student voices for understanding the student experience, this book will inform strategies for quality improvement in higher education globally.With contributions, representing an international community of academics, educational developers, institutional data analysts and student-researchers, this book reflects on the role of computer-aided text analysis in gaining insight of student views. The chapters explore the applications of text-mining in different forms, these include varied institutional contexts, using a range of instruments and pursuing different institutional aims and objectives. Contributors provide insights enabled by computer-aided analysis in distilling the student voice and turning large volumes of data into useful information and knowledge to inform actions. Practical tips and core principles are explored to assist academic institutions when embarking on analysing qualitative student feedback.Written for a wide audience, Analysing Student Feedback in Higher Education provides those making informed decisions about how to approach analyses of large volumes of student narratives, with the benefit of learning from the experiences of those who already started treading this path. It enables academic developers, institutional researchers, academics, and administrators to see how bringing text mining to their institutions can help them in better understanding and using the student voice to improve practice.

Programmes in higher education tend to focus attention on the student’s first year (because of concerns about student transition and retention) and on their final year (because of student exiting for their future careers). The middle year(s) of programmes receive relatively little attention which can often lead to a slump in student development at a crucial point in their studies. Stepping up to the Second Year at University provides practical advice that can be implemented by staff throughout higher education institutions. Rather than providing a set of prescriptions to be slavishly implemented, it prompts practitioners to think constructively about curriculum design and delivery, and about maximising student potential within the context of their particular institution. Amongst the questions asked in this book are: In what way do students’ perceptions of their experience shift as they progress through the first two years of study? How do psychological factors bear on student engagement and performance in the second year? What in the second-year curriculum might need greater attention? How can the analysis of institutional data help? This book, builds on critiques existing international research on the mid-years experience and also features evidence from a significant new research project from Liverpool John Moores University, provides a number of starting points for institutions’ enhancement activities as they seek to make the experience of their students as rewarding as possible. It is a must read for institutional managers of higher academic programmes, higher education practitioners and anyone interested in the development of teaching at higher education level.

Programmes in higher education tend to focus attention on the student’s first year (because of concerns about student transition and retention) and on their final year (because of student exiting for their future careers). The middle year(s) of programmes receive relatively little attention which can often lead to a slump in student development at a crucial point in their studies. Stepping up to the Second Year at University provides practical advice that can be implemented by staff throughout higher education institutions. Rather than providing a set of prescriptions to be slavishly implemented, it prompts practitioners to think constructively about curriculum design and delivery, and about maximising student potential within the context of their particular institution. Amongst the questions asked in this book are: In what way do students’ perceptions of their experience shift as they progress through the first two years of study? How do psychological factors bear on student engagement and performance in the second year? What in the second-year curriculum might need greater attention? How can the analysis of institutional data help? This book, builds on critiques existing international research on the mid-years experience and also features evidence from a significant new research project from Liverpool John Moores University, provides a number of starting points for institutions’ enhancement activities as they seek to make the experience of their students as rewarding as possible. It is a must read for institutional managers of higher academic programmes, higher education practitioners and anyone interested in the development of teaching at higher education level.

Computational intelligence is rapidly becoming an essential part of reliability engineering. Scientists benefit from this book by gaining insight in the latest in the merger of reliability engineering and computational intelligence.

Computational intelligence is rapidly becoming an essential part of reliability engineering. Scientists benefit from this book by gaining insight in the latest in the merger of reliability engineering and computational intelligence.

This book offers insight into the current issues of the merger between reliability engineering and computational intelligence. The intense development of information technology allows for designing more complex systems as well as creating more detailed models of real-world systems which forces traditional reliability engineering approaches based on Boolean algebra, probability theory, and statistics to embrace the world of data science. The works deal with methodological developments as well as applications in the development of safe and reliable systems in various kinds of distribution networks, in the development of highly reliable healthcare systems, in finding weaknesses in systems with the human factor, or in reliability analysis of large information systems and other software solutions. In this book, experts from various fields of reliability engineering and computational intelligence present their view on the risks, the opportunities and the synergy between reliability engineering and computational intelligence that have been developed separately but in recent years have found a way to each other. The topics addressed include the latest advances in computing technology to improve the real lives of millions of people by increasing safety and reliability of various types of real-life systems by increasing the availability of software services, reducing the accident rate of means of transport, developing high reliable patient-specific health care, or generally, save cost and increase efficiency in the work and living environment. Though this book, the reader has access to professionals and researchers in the fields of reliability engineering and computational intelligence that share their experience in merging the two as well as an insight into the latest methods, concerns and application domains.

This book offers insight into the current issues of the merger between reliability engineering and computational intelligence. The intense development of information technology allows for designing more complex systems as well as creating more detailed models of real-world systems which forces traditional reliability engineering approaches based on Boolean algebra, probability theory, and statistics to embrace the world of data science. The works deal with methodological developments as well as applications in the development of safe and reliable systems in various kinds of distribution networks, in the development of highly reliable healthcare systems, in finding weaknesses in systems with the human factor, or in reliability analysis of large information systems and other software solutions. In this book, experts from various fields of reliability engineering and computational intelligence present their view on the risks, the opportunities and the synergy between reliability engineering and computational intelligence that have been developed separately but in recent years have found a way to each other. The topics addressed include the latest advances in computing technology to improve the real lives of millions of people by increasing safety and reliability of various types of real-life systems by increasing the availability of software services, reducing the accident rate of means of transport, developing high reliable patient-specific health care, or generally, save cost and increase efficiency in the work and living environment. Though this book, the reader has access to professionals and researchers in the fields of reliability engineering and computational intelligence that share their experience in merging the two as well as an insight into the latest methods, concerns and application domains.

This book contains an interesting and state-of the art collection of chapters presenting several examples of attempts to developing modern tools utilizing computational intelligence in different real life problems encountered by humans. Reasoning, prediction, modeling, optimization, decision making, etc. need modern, soft and intelligent algorithms, methods and methodologies to solve, in the efficient ways, problems appearing in human activity. The contents of the book is divided into two parts. Part I, consisting of four chapters, is devoted to selected links of computational intelligence, medicine, health care and biomechanics. Several problems are considered: estimation of healthcare system reliability, classification of ultrasound thyroid images, application of fuzzy logic to measure weight status and central fatness, and deriving kinematics directly from video records. Part II, also consisting of four chapters, is devoted to selected links of computational intelligence and biology. The common denominator of three chapters is Physarum polycephalum, one-cell organisms able to build complex networks for solving different computational tasks. One chapter focuses on a novel device, the memristor, that has possible uses both in the creation of hardware neural nets for artificial intelligence and as the connection between a hardware neural net and a living neuronal cell network in the treatment and monitoring of neurological disease. This book is intended for a wide audience of readers who are interested in various aspects of computational intelligence.

This book presents latest results and selected applications of Computational Intelligence in Biomedical Technologies. Most of contributions deal with problems of Biomedical and Medical Informatics, ranging from theoretical considerations to practical applications. Various aspects of development methods and algorithms in Biomedical and Medical Informatics as well as Algorithms for medical image processing, modeling methods are discussed. Individual contributions also cover medical decision making support, estimation of risks of treatments, reliability of medical systems, problems of practical clinical applications and many other topics. This book is intended for scientists interested in problems of Biomedical Technologies, for researchers and academic staff, for all dealing with Biomedical and Medical Informatics, as well as PhD students. Useful information is offered also to IT companies, developers of equipment and/or software for medicine and medical professionals.

This book contains an interesting and state-of the art collection of chapters presenting several examples of attempts to developing modern tools utilizing computational intelligence in different real life problems encountered by humans. Reasoning, prediction, modeling, optimization, decision making, etc. need modern, soft and intelligent algorithms, methods and methodologies to solve, in the efficient ways, problems appearing in human activity. The contents of the book is divided into two parts. Part I, consisting of four chapters, is devoted to selected links of computational intelligence, medicine, health care and biomechanics. Several problems are considered: estimation of healthcare system reliability, classification of ultrasound thyroid images, application of fuzzy logic to measure weight status and central fatness, and deriving kinematics directly from video records. Part II, also consisting of four chapters, is devoted to selected links of computational intelligence and biology. The common denominator of three chapters is Physarum polycephalum, one-cell organisms able to build complex networks for solving different computational tasks. One chapter focuses on a novel device, the memristor, that has possible uses both in the creation of hardware neural nets for artificial intelligence and as the connection between a hardware neural net and a living neuronal cell network in the treatment and monitoring of neurological disease. This book is intended for a wide audience of readers who are interested in various aspects of computational intelligence.