Stefanie Lindstaedt

Bio: Stefanie Lindstaedt is an academic researcher from Graz University of Technology. The author has contributed to research in topics: Organizational learning & Ontology (information science). The author has an hindex of 22, co-authored 173 publications receiving 1803 citations. Previous affiliations of Stefanie Lindstaedt include University of Graz & University of Colorado Boulder.

Modeling competencies for supporting work‐integrated learning in knowledge work

Abstract: Purpose – The purpose of this paper is to suggest a way to support work‐integrated learning for knowledge work, which poses a great challenge for current research and practice.Design/methodology/approach – The authors first suggest a workplace learning context model, which has been derived by analyzing knowledge work and the knowledge sources used by knowledge workers. The authors then focus on the part of the context that specifies competencies by applying the competence performance approach, a formal framework developed in cognitive psychology. From the formal framework, a methodology is then derived of how to model competence and performance in the workplace. The methodology is tested in a case study for the learning domain of requirements engineering.Findings – The Workplace Learning Context Model specifies an integrative view on knowledge workers' work environment by connecting learning, work and knowledge spaces. The competence performance approach suggests that human competencies be formalized with...

MoKi: The Enterprise Modelling Wiki

Abstract: Enterprise modelling focuses on the construction of a structured description, the so-called enterprise model , which represents aspects relevant to the activity of an enterprise Although it has become clearer recently that enterprise modelling is a collaborative activity, involving a large number of people, most of the enterprise modelling tools still only support very limited degrees of collaboration Within this contribution we describe a tool for enterprise modelling, called MoKi (MOdelling wiKI), which supports agile collaboration between all different actors involved in the enterprise modelling activities MoKi is based on a Semantic Wiki and enables actors with different expertise to develop an enterprise model not only using structural (formal) descriptions but also adopting more informal and semi-formal descriptions of knowledge

Sustaining Tel: From Innovation to Learning and Practice

Abstract: Wolpers, M., Kirschner, P. A., Scheffel, M., Lindstaedt, S., & Dimitrova, V. (Eds.) (2010). Sustaining TEL: From Innovation to learning and practice. Proceedings of the 5th European Conference on Technology Enhanced Learning, EC-TEL 2010. September, 28 - October, 1, 2010, Barcelona, Spain. Berlin: Springer Verlag.

21st Century Learning for 21st Century Skills

Abstract: I want to argue in this lecture, that life – especially educational life – is never that simple. What exactly are 21 century skills? How, for example, do they differ from ‘knowledge’? And once we know what they are, does there follow a strategy – or at least a set of principles – for what learning should look like, and the roles we ascribe to technology? Most importantly, if 21 century knowledge is qualitatively different from the 19 and 20 century knowledge that characterises much of our existing curricula, we will need to consider carefully just how to make that knowledge learnable and accessible through the design of digital technologies and their evaluation.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Out of the Crisis

Abstract: According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.